Cell population analysis

ABSTRACT

A method of analysis using mass spectrometry and/or ion mobility spectrometry is disclosed comprising: (a) using a first device to generate smoke, aerosol or vapour from a target in vitro or ex vivo cell population; (b) mass analysing and/or ion mobility analysing said smoke, aerosol or vapour, or ions derived therefrom, in order to obtain spectrometric data; and (c) analysing said spectrometric data in order to identify and/or characterise said target cell population or one or more cells and/or compounds present in said target cell population.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 15/555,694, filed Sep. 5, 2017, which is the U.S.National Phase of International Application number PCT/GB2016/050603,filed Mar. 7, 2016, which claims priority from and the benefit of UnitedKingdom patent application No. 1503876.3 filed on 6 Mar. 2015, UnitedKingdom patent application No. 1503864.9 filed on 6 Mar. 2015, UnitedKingdom patent application No. 1518369.2 filed on 16 Oct. 2015, UnitedKingdom patent application No. 1503877.1 filed on 6 Mar. 2015, UnitedKingdom patent application No. 1503867.2 filed on 6 Mar. 2015, UnitedKingdom patent application No. 1503863.1 filed on 6 Mar. 2015, UnitedKingdom patent application No. 1503878.9 filed on 6 Mar. 2015, UnitedKingdom patent application No. 1503879.7 filed on 6 Mar. 2015 and UnitedKingdom patent application No. 1516003.9 filed on 9 Sep. 2015. Theentire contents of these applications are incorporated herein byreference.

FIELD OF THE INVENTION

The present invention relates generally to mass spectrometers and/or ionmobility spectrometers and in particular to methods of mass analysingand/or ion mobility analysing ex vivo or in vitro cell populations ormedium derived therefrom using an ambient ionisation mass spectrometrymethod such as Rapid Evaporative ionisation Mass Spectrometry (“REIMS”)ion source. Particular applications include cell identification, cellcharacterisation, cellular process analysis, pharmaceutical compounddiscovery and pharmaceutical compound production.

BACKGROUND

Rapid Evaporative Ionization Mass Spectrometry (“REIMS”) is an ambientmass spectrometric method which was recently developed forintra-operative tissue identification. In case of REIMS analysis,biological samples are rapidly heated up via Joule-heating and theresulting aerosol is directly transferred into the mass spectrometer. Itwas found that electrosurgical tools such as monopolar electroscalpelsas used in many open surgeries or the bipolar forceps as commonlyapplied in brain surgery can serve as ion sources following the REI(rapid evaporation ionization) mechanism. A chemical fingerprint of thesample is recorded by the mass spectrometric analysis of the chargedparticles carried by the aerosol created during ionization. REIMSprofiles mainly display complex phospholipid species originating fromthe cell membranes and were shown to be highly specific to thehistological or histopathological type of the tissues. More recently,the REIMS methodology has been developed to characterize and identifymicroorganisms including bacteria and fungi with excellent accuracies atspecies, genus and Gram-level.

REIMS profiles have allowed strain-level differentiation of sevenEscherichia coli strains with an overall 88% accuracy independent ofculturing conditions or the age of colonies. Reference is made toStrittmatter et al. Anal. Chem. 2014, 86, 6555-6562.

A fundamental aim is linking lipidomic profile to phenotype. Thetraditional technique of choice for lipidomic profiling is liquidchromatography-mass spectrometry (LC-MS). However, even using state ofthe art ultra-high performance liquid chromatography (UPLC-MS), run-timeper sample is still in the range of 10-20 minutes and the analysisrequires extensive sample preparation (homogenization, extraction,etc.).

Several mass spectrometric profiling methodologies have been developedin the recent past. Ambient mass spectrometric methods such as the mostwidely used Desorption Electrospray Ionization Mass Spectrometry(DESI-MS) offer the capabilities to analyze samples in their nativestate without any significant sample preparation steps. These ambientlipid profiling technologies have recently been deployed in cancertissue studies to characterize the lipid composition of breast cancercompared to normal breast tissue, the identification of cancermetastasis within lymph nodes, colorectal cancer compared to normalmucosa, and brain cancer among others. A complementary approach forstudying the molecular background of histologically specific lipidprofiles would involve the use of cell lines, which would address sampleavailability and standardization of sampling, lift most of the ethicalconstraints and also allow functional testing including gene silencingor metabolic flux analysis.

Cell lines are a popular means of studying various biochemical anddisease processes in vitro. In the case of cancer studies, the celllines provide a means to study cancer development and progression aswell as the investigation of pathobiochemical processes as close to thehuman body as possible while still allowing free manipulation ofexperimental parameters.

One of the most extensively characterized cell line collections is theNCI-60 cell line panel compiled by the National Cancer institute as partof the In Vitro Cell Line Screening Project (Robert H. Shoemaker “TheNCI60 human tumour cell line anticancer drug screen” Nature ReviewsCancer 6, 813-823, October 2006). The panel comprises 60 human cancerouscell lines from nine different organs of origin, namely leukemia,melanoma, cancers of the lung, colon, brain, ovary, breast, prostate,and kidney.

Data available for these cell lines includes drug sensitivity patternsfor more than 100,000 compounds and natural products, global protein andgene expression data and common mutations associated with cancer(Weinstein “Integromic analysis of the NCI-60 cancer cell lines” BreastDis. 2004; 19:11-22). However, the associated metabolomics andlipidomics data is comparatively sparse. This represents a striking gapin the cancer-related biochemical data.

Complex lipids are the main constituents of cell membranes and playimportant functional, structural, and metabolic roles by acting assignaling molecules (e.g., PI phosphates, ceramides, lysophosphatidicacids (LPA)) or as precursors for secondary messengers (e.g., inositoltriphosphate (IP3)/diacylglycerol (DAG)). Changes in the membrane lipidcomposition can regulate function and availability of intrinsic membraneproteins and affect cell signaling mechanisms.

Cancers figure among the leading causes of morbidity and mortalityworldwide, with approximately 14 million new cases and 8.2 millioncancer related deaths in 2012. According to the World HealthOrganisation, the number of new cases is expected to rise by about 70%over the next 2 decades.

Gastro-intestinal cancers are a leading cause of mortality and accountfor 23% of cancer-related deaths worldwide. In order to improve outcomesfrom cancers and other diseases, novel cell characterisation methods areneeded in order to facilitate accurate diagnosis.

Rapid evaporative ionization mass spectrometry (“REIMS”) is a technologywhich has recently been developed for the real time identification oftissues during surgical interventions. Coupling of REIMS technology withhandheld sampling devices has resulted in iKnife sampling technology,which can provide intra-operative tissue identification with an accuracyof 92-100%.

The iKnife sampling technology allows surgeons to more efficientlyresect tumours intra-operatively through minimizing the amount ofhealthy tissue removed whilst ensuring that all the cancerous tissue isremoved.

REIMS analysis of biological tissue has been shown to yield phospholipidprofiles showing high histological and histopathologicalspecificity—similar to techniques using Matrix Assisted Laser DesorptionIonisation (“MALDI”), Secondary Ion Mass Spectrometry (“SIMS”),Desorption Electrospray Ionisation (“DESI”) imaging, jet desorptionionisation (“JeDI”), laser desorption ionisation (“LDI”), plasmaassisted desorption ionization (“PADI”), desorption atmospheric pressurephotoionisation (“DAPPI”), and easy ambient sonic-spray ionisation(“EASI”). A mass spectrometric signal is obtained by subjecting thecellular biomass to alternating electric current at radiofrequency whichcauses localized Joule-heating and the disruption of cells along withdesorption of charged and neutral particles. The resulting aerosol orsurgical smoke is then transported to a mass spectrometer for on-linemass spectrometric analysis.

The known REIMS technique is typically performed on external tissues ortissues accessed through surgery.

Conventional methods of screening for potential therapeutic agents whichinteract with cell lines are known.

It is desired to provide improved methods of ambient ionisation massand/or ion mobility spectrometry and apparatus for performing ambientionisation mass and/or ion mobility spectrometry.

SUMMARY

The present invention provides a method of analysis using mass and/orion mobility spectrometry comprising:

-   -   (a) using a first device to generate smoke, aerosol or vapour        from a target in vitro or ex vivo cell population and/or medium        derived therefrom;    -   (b) mass analysing and/or ion mobility analysing said smoke,        aerosol or vapour, or ions derived therefrom, in order to obtain        spectrometric data; and    -   (c) analysing said spectrometric data in order to identify        and/or characterise one or more cells and/or compounds present        in said target cell population and/or medium derived therefrom.

Optional further details of the invention are provided in the detaileddescription and in the claims.

Various embodiments are contemplated wherein analyte ions are generatedfrom the target, smoke, aerosol or vapour, e.g., by an ambientionisation ion source. The analyte ions, or ions derived therefrom, maybe subjected either to: (i) mass analysis by a mass analyser such as aquadrupole mass analyser or a Time of Flight mass analyser; (ii) ionmobility analysis (IMS) and/or differential ion mobility analysis (DMA)and/or Field Asymmetric Ion Mobility Spectrometry (FAIMS) analysis;and/or (iii) a combination of firstly ion mobility analysis (IMS) and/ordifferential ion mobility analysis (DMA) and/or Field Asymmetric IonMobility Spectrometry (FAIMS) analysis followed by secondly massanalysis by a mass analyser such as a quadrupole mass analyser or a Timeof Flight mass analyser (or vice versa). Various embodiments also relateto an ion mobility spectrometer and/or mass analyser and a method of ionmobility spectrometry and/or method of mass analysis.

The mass and/or ion mobility spectrometer may obtain data in negativeion mode only, positive ion mode only, or in both positive and negativeion modes. Positive ion mode spectrometric data may be combined orconcatenated with negative ion mode spectrometric data. Negative ionmode can provide particularly useful spectra for classifying aerosol,smoke or vapour samples, such as aerosol, smoke or vapour samples fromtargets comprising lipids.

Ion mobility spectrometric data may be obtained using different ionmobility drift gases, or dopants may be added to the drift gas to inducea change in drift time of one or more species. This data may then becombined or concatenated.

Other embodiments are contemplated wherein the first device forgenerating aerosol, smoke or vapour from the target may comprise anargon plasma coagulation (“APC”) device. An argon plasma coagulationdevice involves the use of a jet of ionised argon gas (plasma) that isdirected through a probe. The probe may be passed through an endoscope.Argon plasma coagulation is essentially a non-contact process as theprobe is placed at some distance from the target. Argon gas is emittedfrom the probe and is then ionized by a high voltage discharge (e.g., 6kV). High-frequency electric current is then conducted through the jetof gas, resulting in coagulation of the target on the other end of thejet. The depth of coagulation is usually only a few millimetres.

The first device, surgical or electrosurgical tool, device or probe orother sampling device or probe disclosed in any of the aspects orembodiments herein may comprise a non-contact surgical device, such asone or more of a hydrosurgical device, a surgical water jet device, anargon plasma coagulation device, a hybrid argon plasma coagulationdevice, a water jet device and a laser device.

A non-contact surgical device may be defined as a surgical devicearranged and adapted to dissect, fragment, liquefy, aspirate, fulgurateor otherwise disrupt biologic tissue without physically contacting thetissue. Examples include laser devices, hydrosurgical devices, argonplasma coagulation devices and hybrid argon plasma coagulation devices.

As the non-contact device may not make physical contact with the tissue,the procedure may be seen as relatively safe and can be used to treatdelicate tissue having low intracellular bonds, such as skin or fat.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments will now be described, by way of example only, andwith reference to the accompanying drawings in which:

FIGS. 1A-1C show an experimental setup used for REIMS analysis of a cellpopulation and/or medium derived therefrom which may be used in a methodprovided herein, FIG. 1D shows an interface for ionising aerosol fromthe target cells and/or medium, and FIGS. 1E-1F show a DESI method foranalysing target cells and/or medium;

FIG. 2 shows the experimental scheme used for assessment of REIMSspectral reproducibility;

FIG. 3 shows a PCA plot of NCI-60 cell line panel, m/z 150-1000, withreplicates highlighted;

FIG. 4 shows the results of a comparison of cell line and bulk cancertissue spectra. Shown are m/z values that were found to be significantlyincreased in either bulk cancerous tissue or cancer cell lines. M/zvalues were analysed using one-way ANOVA. The m/z that are significantlyincreased in either bulk cancerous tissue or cancer cell lines are m/z644.44 (p=1.3919e−05), m/z 659.46 (p+1.2662e−05), m/z 766.54(p=1.4088e−05), m/z 768.55 (p=1.4088e−05), m/z 794.57 (p=1.4088e−05),m/z 885.54 (p=1.4088e−05), m/z 750.54 (p=1.4088e−05), m/z 790.53(p=2.1978e−05), and m/z 750.54 (p=1.4088e−05);

FIG. 5A shows PE(38:3)/PE(38:2) peak intensity ratio and FIG. 5B showsPE(38:3) peak intensity as a function of FADS2 protein expression (forFADS2 protein expression see Gholami, Amin M., et al., Global ProteomeAnalysis of the NCI-60 Cell Line Panel. Cell Reports, 2013. 4(3): p.609-620);

FIG. 6A shows representative mass spectral profiles between m/z 600-900as obtained for HeLa, MES-SA and SNB cell line pellets respectively; andFIG. 6B shows 3-dimensional PCA plot of averaged REIMS data collectedfrom several independent cultures of HeLa, MES-SA and SNB-19 cells overthe spectral mass range of m/z 600-900, wherein circles, squares andtriangles represent different measurement times (day 1, day 2 or day 3)and shades reflect passage numbers (p4 for 4 passages and p6 for 6passages);

FIG. 7 shows a hierarchical cluster analysis and shows how lipidomicprofiles revealed by REIMS distinguish cell lines of the NCI-60 panelconsisting of ovarian (OV), renal (RE), melanoma (ME), central nervoussystem (CNS), breast (BR), lung (LC), colon (CO), leukemia (LE) andprostate (PR) origin, wherein the cluster dendrogram of the NCI-60 panelincludes independently cultured replicates (highlighted by asterisks) orbiologically related cell lines (arrows) and wherein distance wascalculated using Pearson correlation and agglomeration via the Wardmetric;

FIG. 8 shows a 2-dimensional PCA plot of averaged REIMS data collectedfrom the NCI-60 cells (squares) and cancer tissue samples (circles)wherein the tissue of origin is colon or ovarian;

FIGS. 9A-9D show a comparison of spectral profiles for bulk tissuesamples and cell lines of the corresponding tissue type of origin. FIG.9A shows the mass spectral profile for bulk ovarian cancer tissue, FIG.9B shows a corresponding mass spectral profile for ovarian cancer cellline OVCAR-3, FIG. 9C shows a mass spectral profile for bulk colorectalcancer tissue and FIG. 9D shows a mass spectral profile for colon cancercell line HCT-15;

FIG. 10A shows a histogram of the correlation values between the fads2gene expression profile and binned m/z peak intensities, and FIG. 10Bshows correlation values with fads2 gene expression as a function of m/zvalues, and the putative lipids corresponding to the highly correlatedm/z signals;

FIG. 11A shows binned peak intensity of m/z 786.54 correlated withscaled fads2 gene expression, FIG. 11B shows ratio of intensity valuesof peaks at m/z 768.55 and 770.57 correlated with scaled fads2geneexpression, and FIG. 11C shows relative abundance of peaks m/z 768.55and 770.57 in the raw REIMS data obtained in HL-60 (leukemia, left) andHT-29 (colon, right) cells;

FIG. 12A shows a histogram of the correlation values between ugcg geneexpression levels and m/z peak intensities, and FIG. 12B showscorrelation values with ugcg gene expression as a function of m/zvalues;

FIG. 13 shows MS/MS spectra for parent ions of m/z=842, 844, and 846identified as glycosylated ceramides;

FIG. 14A shows mass spectrometric signal intensity (TIC normalised andlog-transformed) as a function of ugcg gene expression for m/z=842.63,FIG. 14B shows for m/z=844.65, FIG. 14C shows for m/z=846.65, FIG. 14Dshows for m/z=735.53, FIG. 14E shows for m/z=818.63, and FIG. 14F showsfor m/z=872.66;

FIG. 15 shows a method of analysis that comprises building aclassification model according to various embodiments;

FIG. 16 shows a set of reference sample spectra obtained from twoclasses of known reference samples;

FIG. 17 shows a multivariate space having three dimensions defined byintensity axes, wherein the multivariate space comprises pluralreference points, each reference point corresponding to a set of threepeak intensity values derived from a reference sample spectrum;

FIG. 18 shows a general relationship between cumulative variance andnumber of components of a PCA model;

FIG. 19 shows a PCA space having two dimensions defined by principalcomponent axes, wherein the PCA space comprises plural transformedreference points or scores, each transformed reference point or scorecorresponding to a reference point of FIG. 17 ;

FIG. 20 shows a PCA-LDA space having a single dimension or axis, whereinthe LDA is performed based on the PCA space of FIG. 19 , the PCA-LDAspace comprising plural further transformed reference points or classscores, each further transformed reference point or class scorecorresponding to a transformed reference point or score of FIG. 19 .

FIG. 21 shows a method of analysis that comprises using a classificationmodel according to various embodiments;

FIG. 22 shows a sample spectrum obtained from an unknown sample;

FIG. 23 shows the PCA-LDA space of FIG. 20 , wherein the PCA-LDA spacefurther comprises a PCA-LDA projected sample point derived from the peakintensity values of the sample spectrum of FIG. 22 ;

FIG. 24 shows a method of analysis that comprises building aclassification library according to various embodiments;

FIG. 25 shows a method of analysis that comprises using a classificationlibrary according to various embodiments;

FIG. 26 shows peak intensities for m/z=747.52 in Mycoplasma infected andMycoplasma-free cell lines during the duration of the Plasmocin®treatment wherein day 1 corresponds with the original (Mycoplasmapositive or negative) sample, day 2 corresponds with the addition ofPlasmocin®, day 3 corresponds with Plasmocin® still being present, day 4corresponds with the removal of Plasmocin® and wherein day 5 correspondswith all samples being Mycoplasma-free;

FIG. 27A shows a number of significantly higher m/z signals inMycoplasma-infected versus Mycoplasma-free samples in HEK and HeLa celllines. For FIG. 27B Mycoplasma-infected (+) and Mycoplasma-free (−) HEK(rectangle) and HeLa (triangle) cells were either treated (t) oruntreated (u). Samples are shown as a function of PC1 and PC2 of PCAtransformed samples in the space of the 18 overlapping m/z signals. and

FIGS. 28A and 28B show intensities of TIC normalised and log-transformedsignals at m/z=819.52 (corresponding to PG(40:7)) in Mycoplasma-free,Mycoplasma-infected and Plasmocin™ treated samples in HeLa (FIG. 28A)and HEK cell lines (FIG. 28B).

DETAILED DESCRIPTION

Although the present invention has been described with reference topreferred embodiments, it will be understood by those skilled in the artthat various changes in form and detail may be made without departingfrom the scope of the invention as set forth in the accompanying claims.

Mass spectrometry (“MS”) based identification techniques such as ambientionization mass spectrometry are known. Direct ambient ionization massspectrometry, such as REIMS, has emerged as a technology allowingreal-time analysis of targets.

The invention described herein may, for example, be used in or with areal-time, robust characterisation tool which utilises ambientionisation technologies, such as REIMS.

Various embodiments are described in more detail below which in generalrelate to generating smoke, aerosol or vapour from a target (details ofwhich are provided elsewhere herein, e.g., an in vitro or ex vivo cellpopulation or target material derived therefrom) using an ambientionisation ion source. The aerosol, smoke or vapour may then be mixedwith a matrix and aspirated into a vacuum chamber of a mass and/or ionmobility spectrometer. The mixture may be caused to impact upon acollision surface causing the aerosol, smoke or vapour to be ionised byimpact ionisation which results in the generation of analyte ions. Theresulting analyte ions (or fragment or product ions derived from theanalyte ions) may then be mass and/or ion mobility analysed and theresulting mass and/or ion mobility spectrometric data may be subjectedto multivariate analysis or other mathematical treatment in order todetermine one or more properties of the target in real time.

Ambient Ionisation Ion Sources

In any of the methods of the invention a device may be used to generatean aerosol, smoke or vapour from one or more regions of a target(details of which are provided elsewhere herein, e.g., an in vitro or exvivo cell population). The device may comprise an ambient ionisation ionsource which is characterised by the ability to generate analyteaerosol, smoke or vapour from target, optionally with little or nopreparation of the target for analysis. By contrast, other types ofionisation ion sources such as Matrix Assisted Laser DesorptionIonisation (“MALDI”) ion sources require a matrix or reagent to be addedto the sample prior to ionisation.

It will be apparent that the requirement to add a matrix or a reagentdirectly to a sample may prevent the ability to perform in vivo analysisof tissue and also, more generally, prevents the ability to provide arapid simple analysis of target material.

Ambient ionisation techniques are particularly useful since they enablea rapid simple analysis of target material to be performed. Whilst thereis no requirement to add a matrix or reagent to a sample in order toperform ambient ionization techniques, the method may optionally includea step of adding a matrix or reagent to the target (e.g., directly tothe target) prior to analysis. The matrix or reagent may be added to thetarget, e.g., to lyse the cells of the target or to enhance the signaltherefrom during the analysis.

A number of different ambient ionisation techniques are known and areintended to fall within the scope of the present invention. As a matterof historical record, Desorption Electrospray Ionisation (“DESI”) wasthe first ambient ionisation technique to be developed and was disclosedin 2004. Since 2004, a number of other ambient ionisation techniqueshave been developed. These ambient ionisation techniques differ in theirprecise ionisation method but they share the same general capability ofgenerating gas-phase ions directly from samples (e.g., withoutpreparation of the sample for analysis). The various ambient ionisationtechniques which are intended to fall within the scope of the presentinvention may not require any sample preparation for the analysis. As aresult, the various ambient ionisation techniques enable targets to beanalysed without the time, expense and problems associated with adding amatrix or reagent to the target material.

A list of ambient ionisation techniques which are intended to fallwithin the scope of the present invention are given in the followingtable:

Acronym Ionisation technique DESI Desorption electrospray ionizationDeSSI Desorption sonic spray ionization DAPPI Desorption atmosphericpressure photoionization EASI Easy ambient sonic-spray ionization JeDIJet desorption electrospray ionization TM-DESI Transmission modedesorption electrospray ionization LMJ-SSP Liquid microjunction-surfacesampling probe DICE Desorption ionization by charge exchange Nano-DESINanospray desorption electrospray ionization EADESI Electrode-assisteddesorption electrospray ionization APTDCI Atmospheric pressure thermaldesorption chemical ionization V-EASI Venturi easy ambient sonic-sprayionization AFAI Air flow-assisted ionization LESA Liquid extractionsurface analysis PTC-ESI Pipette tip column electrospray ionizationAFADESI Air flow-assisted desorption electrospray ionization DEFFIDesorption electro-flow focusing ionization ESTASI Electrostatic sprayionization PASIT Plasma-based ambient sampling ionization transmissionDAPCI Desorption atmospheric pressure chemical ionization DART Directanalysis in real time ASAP Atmospheric pressure solid analysis probeAPTDI Atmospheric pressure thermal desorption ionization PADI Plasmaassisted desorption ionization DBDI Dielectric barrier dischargeionization FAPA Flowing atmospheric pressure afterglow HAPGDI Heliumatmospheric pressure glow discharge ionization APGDDI Atmosphericpressure glow discharge desorption ionization LTP Low temperature plasmaLS-APGD Liquid sampling-atmospheric pressure glow discharge MIPDIMicrowave induced plasma desorption ionization MFGDP Microfabricatedglow discharge plasma RoPPI Robotic plasma probe ionization PLASI Plasmaspray ionization MALDESI Matrix assisted laser desorption electrosprayionization ELDI Electrospray laser desorption ionization LDTD Laserdiode thermal desorption LAESI Laser ablation electrospray ionizationCALDI Charge assisted laser desorption ionization LA-FAPA Laser ablationflowing atmospheric pressure afterglow LADESI Laser assisted desorptionelectrospray ionization LDESI Laser desorption electrospray ionizationLEMS Laser electrospray mass spectrometry LSI Laser spray ionizationIR-LAMICI Infrared laser ablation metastable induced chemical ionizationLDSPI Laser desorption spray post-ionization PAMLDI Plasma assistedmultiwavelength laser desorption ionization HALDI High voltage-assistedlaser desorption ionization PALDI Plasma assisted laser desorptionionization ESSI Extractive electrospray ionization PESI Probeelectrospray ionization ND-ESSI Neutral desorption extractiveelectrospray ionization PS Paper spray DIP-APCI Direct inletprobe-atmospheric pressure chemical ionization TS Touch spray Wooden-tipWooden-tip electrospray CBS-SPME Coated blade spray solid phasemicroextraction TSI Tissue spray ionization RADIO Radiofrequencyacoustic desorption ionization LIAD-ESI Laser induced acousticdesorption electrospray ionization SAWN Surface acoustic wavenebulization UASI Ultrasonication-assisted spray ionization SPA-nanoESISolid probe assisted nanoelectrospray ionization PAUSI Paper assistedultrasonic spray ionization DPESI Direct probe electrospray ionizationESA-Py Electrospray assisted pyrolysis ionization APPIS Ambient pressurepyroelectric ion source RASTIR Remote analyte sampling transport andionization relay SACI Surface activated chemical ionization DEMIDesorption electrospray metastable-induced ionization REIMS Rapidevaporative ionization mass spectrometry SPAM Single particle aerosolmass spectrometry TDAMS Thermal desorption-based ambient massspectrometry MAII Matrix assisted inlet ionization SAII Solvent assistedinlet ionization SwiFERR Switched ferroelectric plasma ionizer LPTDLeidenfrost phenomenon assisted thermal desorption

According to an embodiment the ambient ionisation ion source maycomprise a rapid evaporative ionisation mass spectrometry (“REIMS”) ionsource wherein a RF voltage is applied to one or more electrodes inorder to generate smoke, aerosol or vapour by Joule heating.

However, it will be appreciated that other ambient ion sources includingthose referred to above may also be utilised. For example, according toanother embodiment the ambient ionisation ion source may comprise alaser ionisation ion source. According to an embodiment the laserionisation ion source may comprise a mid-IR laser ablation ion source.For example, there are several lasers which emit radiation close to orat 2.94 μm which corresponds with the peak in the water absorptionspectrum. According to various embodiments the ambient ionisation ionsource may comprise a laser ablation ion source having a wavelengthclose to 2.94 μm on the basis of the high absorption coefficient ofwater at 2.94 μm. According to an embodiment the laser ablation ionsource may comprise a Er:YAG laser which emits radiation at 2.94 μm.

Other embodiments are contemplated wherein a mid-infrared opticalparametric oscillator (“OPO”) may be used to produce a laser ablationion source having a longer wavelength than 2.94 μm. For example, anEr:YAG pumped ZGP-OPO may be used to produce laser radiation having awavelength of e.g. 6.1 μm, 6.45 μm or 6.73 μm. In some situations it maybe advantageous to use a laser ablation ion source having a shorter orlonger wavelength than 2.94 μm since only the surface layers will beablated and less thermal damage may result. According to an embodiment aCo:MgF₂ laser may be used as a laser ablation ion source wherein thelaser may be tuned from 1.75-2.5 μm. According to another embodiment anoptical parametric oscillator (“OPO”) system pumped by a Nd:YAG lasermay be used to produce a laser ablation ion source having a wavelengthbetween 2.9-3.1 μm. According to another embodiment a CO₂ laser having awavelength of 10.6 μm may be used to generate the aerosol, smoke orvapour.

According to other embodiments the ambient ionisation ion source maycomprise an ultrasonic ablation ion source or a hybridelectrosurgical-ultrasonic ablation source that generates a liquidsample which is then aspirated as an aerosol. The ultrasonic ablationion source may comprise a focused or unfocussed ultrasound.

According to an embodiment the first device for generating aerosol,smoke or vapour from the target may comprise a tool which utilises an RFvoltage, such as a continuous RF waveform. According to otherembodiments a radiofrequency system may be used which is arranged tosupply pulsed plasma RF energy to a tool. The tool may comprise, forexample, a PlasmaBlade®. Pulsed plasma RF tools operate at lowertemperatures than conventional electrosurgical tools (e.g. 40-170° C.c.f. 200-350° C.) thereby reducing thermal damage depth.

Pulsed waveforms and duty cycles may be used for both cut andcoagulation modes of operation by inducing electrical plasma along thecutting edge(s) of a thin insulated electrode.

According to an embodiment the first device comprises a surgicalwater/saline jet device such as a resection device, a hybrid of suchdevice with any of the other devices herein, an electrosurgery argonplasma coagulation device, a hybrid argon plasma coagulation andwater/saline jet device.

According to an embodiment the first device comprises or forms part ofan ambient ion or ionisation source; or said first device generates saidaerosol, smoke or vapour from the target and contains ions and/or issubsequently ionised by an ambient ion or ionisation source, or otherionisation source.

Optionally, the first device comprises or forms part of a device, or anion source, selected from the group consisting of: (i) a rapidevaporative ionisation mass spectrometry (“REIMS”) ion source; (ii) adesorption electrospray ionisation (“DESI”) ion source; (iii) a laserdesorption ionisation (“LDI”) ion source; (iv) a thermal desorption ionsource; (v) a laser diode thermal desorption (“LDTD”) ion source; (vi) adesorption electro-flow focusing (“DEFFI”) ion source; (vii) adielectric barrier discharge (“DBD”) plasma ion source; (viii) anAtmospheric Solids Analysis Probe (“ASAP”) ion source; (ix) anultrasonic assisted spray ionisation ion source; (x) an easy ambientsonic-spray ionisation (“EASI”) ion source; (xi) a desorptionatmospheric pressure photoionisation (“DAPPI”) ion source; (xii) apaperspray (“PS”) ion source: (xiii) a jet desorption ionisation(“JeDI”) ion source; (xiv) a touch spray (“TS”) ion source; (xv) anano-DESI ion source; (xvi) a laser ablation electrospray (“LAESI”) ionsource; (xvii) a direct analysis in real time (“DART”) ion source;(xviii) a probe electrospray ionisation (“PESI”) ion source; (xix) asolid-probe assisted electrospray ionisation (“SPA-ESI”) ion source;(xx) a cavitron ultrasonic surgical aspirator (“CUSA”) device; (xxi) ahybrid CUSA-diathermy device; (xxii) a focused or unfocussed ultrasonicablation device; (xxiii) a hybrid focused or unfocussed ultrasonicablation and diathermy device; (xxiv) a microwave resonance device;(xxv) a pulsed plasma RF dissection device; (xxvi) an argon plasmacoagulation device; (xxvi) a hybrid pulsed plasma RF dissection andargon plasma coagulation device; (xxvii) a hybrid pulsed plasma RFdissection and JeDI device; (xxviii) a surgical water/saline jet device;(xxix) a hybrid electrosurgery and argon plasma coagulation device; and(xxx) a hybrid argon plasma coagulation and water/saline jet device.

Optionally, the step of using said first device to generate aerosol,smoke or vapour comprises contacting said target with one or moreelectrodes.

Optionally, said one or more electrodes comprise either: (i) a monopolardevice, wherein there is optionally provided a separate returnelectrode; (ii) a bipolar device; or (iii) a multi-phase RF device,wherein there is optionally provided at least one separate returnelectrode.

Optionally, said one or more electrodes comprise or forms part of arapid evaporation ionization mass spectrometry (“REIMS”) device.

Optionally, said method further comprises applying an AC or RF voltageto said one or more electrodes in order to generate said aerosol, smokeor vapour.

Optionally, the step of applying said AC or RF voltage to said one ormore electrodes further comprises applying one or more pulses of said ACor RF voltage to said one or more electrodes.

Optionally, said step of applying said AC or RF voltage to said one ormore electrodes causes heat to be dissipated into said target.

Optionally, said step of using said first device to generate aerosol,smoke or vapour from one or more regions of the target further comprisesirradiating the target with a laser.

Optionally, said first device generates aerosol from one or more regionsof the target by direct evaporation or vaporisation of target materialfrom said target by Joule heating or diathermy.

Optionally, said step of using said first device to generate aerosol,smoke or vapour from one or more regions of the target further comprisesdirecting ultrasonic energy into said target.

Optionally, said aerosol comprises uncharged aqueous droplets. Thedroplets may comprise cellular material.

Optionally, at least 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%of the mass or matter generated by said first device and which formssaid aerosol may be in the form of droplets.

The first device may be arranged and adapted to generate aerosol whereinthe Sauter mean diameter (“SMD”, d32) of said aerosol is in a range:(i)<5 μm; (ii) 5-10 μm; (iii) 10-15 μm; (iv) 15-20 μm; (v) 20-25 μm; or(vi) >25 μm.

The aerosol may traverse a flow region with a Reynolds number (Re) inthe range: (i)<2000; (ii) 2000-2500; (iii) 2500-3000; (iv) 3000-3500;(v) 3500-4000; or (vi) >4000.

Substantially at the point of generating the aerosol, the aerosol maycomprise droplets having a Weber number (We) selected from the groupconsisting of: (i)<50; (ii) 50-100; (iii) 100-150; (iv) 150-200; (v)200-250; (vi) 250-300; (vii) 300-350; (viii) 350-400; (ix) 400-450; (x)450-500; (xi) 500-550; (xii) 550-600; (xiii) 600-650; (xiv) 650-700;(xv) 700-750; (xvi) 750-800; (xvii) 800-850; (xviii) 850-900; (xix)900-950; (xx) 950-1000; and (xxi) >1000.

Substantially at the point of generating the aerosol, the aerosol maycomprise droplets having a Stokes number (Sk) in the range: (i) 1-5;(ii) 5-10; (iii) 10-15; (iv) 15-20; (v) 20-25; (vi) 25-30; (vii) 30-35;(viii) 35-40; (ix) 40-45; (x) 45-50; and (xi) >50.

Substantially at the point of generating the aerosol, the aerosol maycomprise droplets having a mean axial velocity selected from the groupconsisting of: (i)<20 m/s; (ii) 20-30 m/s; (iii) 30-40 m/s; (iv) 40-50m/s; (v) 50-60 m/s; (vi) 60-70 m/s; (vii) 70-80 m/s; (viii) 80-90 m/s;(ix) 90-100 m/s; (x) 100-110 m/s; (xi) 110-120 m/s; (xii) 120-130 m/s;(xiii) 130-140 m/s; (xiv) 140-150 m/s; and (xv) >150 m/s.

Optionally, said aerosol comprises uncharged aqueous droplets. Thedroplets may comprise cellular material.

Optionally, the method comprises ionising at least some of said aerosol,smoke or vapour, or analyte therein, so as to generate analyte ions;wherein said analyte ions are analysed to obtain said spectrometricdata.

Optionally, the method comprises directing or aspirating at least someof said aerosol, smoke or vapour into a vacuum chamber of a mass and/orion mobility spectrometer; and/or ionising at least some said aerosol,smoke or vapour, or the analyte therein, within a, or said, vacuumchamber of said spectrometer so as to generate a plurality of analyteions.

Optionally, the method comprises causing said aerosol, smoke or vapour,or analyte therein, to impact upon a collision surface, optionallylocated within a, or the, vacuum chamber of said spectrometer, so as togenerate the plurality of analyte ions.

Optionally, the collision surface may be heated. The collision surfacemay be heated to a temperature selected from the group consisting of:(i) about <100° C.; (ii) about 100-200° C.; (iii) about 200-300° C.;(iv) about 300-400° C.; (v) about 400-500° C.; (vi) about 500-600° C.;(vii) about 600-700° C.; (viii) about 700-800° C.; (ix) about 800-900°C. (x) about 900-1000° C.; (xi) about 1000-1100° C.; and (xii)about >1100° C.

Optionally, the method comprises adding a matrix to said aerosol, smokeor vapour; optionally wherein said matrix is selected from the groupconsisting of: (i) a solvent for said aerosol, smoke or vapour oranalyte therein; (ii) an organic solvent; (iii) a volatile compound;(iv) polar molecules; (v) water; (vi) one or more alcohols; (vii)methanol; (viii) ethanol; (ix) isopropanol; (x) acetone; (xi)acetonitrile; (xii) 1-butanol; (xiii) tetrahydrofuran; (xiv) ethylacetate; (xv) ethylene glycol; (xvi) dimethyl sulfoxide; an aldehyde;(xviii) a ketone; (xiv) non-polar molecules; (xx) hexane; (xxi)chloroform; and (xxii) propanol.

Optionally, the method may be carried out using negative ion mode, sooptionally, the method comprises analysing spectrometric data obtainedusing negative ion mode. Optionally, the method may be carried out usingpositive ion mode, so optionally, the method comprises analysingspectrometric data obtained using positive ion mode. Optionally, themethod comprises analysing spectrometric data obtained using negativeion mode and analysing spectrometric data obtained using positive ionmode.

The mass and/or ion mobility spectrometer may obtain data in negativeion mode only, positive ion mode only, or in both positive and negativeion modes. Positive ion mode spectrometric data may be combined withnegative ion mode spectrometric data.

Ion mobility spectrometric data may be obtained using different ionmobility drift gases. This data may then be combined.

The matrix and/or aerosol, smoke or vapour may be doped with one or moreadditives to, for example, enhance the solvation or dilution of analytewith the matrix, or for enhancing the ionisation of the analyte withinthe aerosol, smoke or vapour.

The doping compound may be an acidic or basic additive such as, forexample, formic acid or diethylamine.

The matrix and/or doping compound may cause derivatisation of theanalyte in the aerosol, smoke or vapour. For example, the matrix and/ordoping compound may cause the derivatisation of cholesterol or steroidsin the analyte. This may render the analyte more easily ionised.

Rapid Evaporative Ionisation Mass Spectrometry (“REIMS”)

Although various different ambient ionisation ion sources may be used inthe invention to analyse a variety of targets, a method of REIMSanalysis on a cell population will now be described in order to assistin understanding the embodiments.

FIG. 1A shows apparatus that may be used to analyse a cell population.The apparatus comprises a pair of handheld electrodes 106,108 in theform of a forceps 102 (i.e. the first device); an RF power supply 103for supplying an RF voltage to the electrodes 106,108; an inlet to amass spectrometer 105; and tubing 104 connecting a port 112 at the rearend of the forceps 102 to the inlet of the spectrometer 105. The forceps102 and RF power supply 103 may be configured such that the forceps 102are bipolar forceps. As shown in FIG. 1B, an open entrance port 110 isprovided in the tip of one of the electrodes 106 at the front of theforceps 102. This entrance port 110 opens up into a conduit 111 withinthe electrode 106. The conduit 111 extends through the electrode 106 toan exit port 112 in the rear of the forceps 102, as shown in FIG. 1C.

As shown in FIG. 1A, the sample/target to be analysed may be provided inthe form of a cell pellet 101. The cell pellet may be provided in acontainer 107 such as an Eppendorf tube. The forceps 102 may be insertedinto contact with the cell pellet 101 so as to obtain biomass from thecell pellet 101 on the tips of the electrodes 106,108. The twoelectrodes 106,108 may be subsequently brought into close proximity witheach other, e.g., by pinching the biomass between the tips of theforceps 102. The RF power supply 103 may be triggered, e.g., using afoot switch, so as to energise the electrodes 106,108. This causes thecell line biomass to be rapidly heated (e.g. by Joule or diathermyheating), due to its non-zero impedance, and smoke, aerosol or vapour tobe emitted from the biomass. The smoke, aerosol or vapour may containcharged molecular species of analytes in the biomass.

The smoke, aerosol or vapour may then be captured or otherwise aspiratedthrough the entrance port 110 and into the conduit 111 in the forceps102. The smoke, aerosol or vapour is then drawn through the conduit 111,out of the exit port 112, along the tubing 104 and into the inlet of themass spectrometer 105. The inherent vacuum system of the massspectrometer may be used to draw the smoke, aerosol or vapour from theentrance port 110 to the inlet of the spectrometer 105. Alternatively, aVenturi device may be used to draw the smoke, aerosol or vapour from theentrance port 110 to the inlet of the spectrometer 105.

FIG. 1D shows a schematic of an embodiment of an interface between thefirst device (e.g., forceps 102) and the mass spectrometer. Theinstrument may comprise an ion analyser 207 having an inlet 206 (whichmay correspond to inlet 5 in FIG. 1A), a vacuum region 208, a collisionsurface 209 and ion optics 212 (such as a Stepwave® ion guide) arrangedwithin the vacuum region 208. The instrument also comprises a sampletransfer tube 202 (corresponding to tubing 4 in FIG. 1 ) and a matrixintroduction conduit 203. The sample transfer tube 202 has an inlet forreceiving the smoke, aerosol or vapour sample 201 (which may correspondto that described in relation to FIG. 1 ) from a sample/target beinginvestigated and an outlet that is connected to the inlet 206 of the ionanalyser 207. The matrix introduction conduit 203 has an inlet forreceiving a matrix compound and an outlet that intersects with thesample transfer tube 202 so as to allow the matrix 204 to be intermixedwith the aerosol sample 201 in the sample transfer tube 202. AT-junction component may be provided at the junction between tubes 202,203 and 206. The tubes 202, 203 and 206 may be removably inserted intothe T-junction.

A method of operating the instrument shown in FIG. 1D will now bedescribed. A sample/target, such as cell population material, may besubjected to the REIMS technique. For example, a first device (e.g.,forceps 102) may be used to generate an aerosol, e.g., as describedabove in relation to FIGS. 1A-1C. The aerosol particles 201 are thenintroduced into the inlet of the sample transfer tube 202. A matrixcompound 204 is introduced into the inlet of the matrix introductionconduit 203. The aerosol particles 201 and matrix compound 204 are drawntowards the inlet 206 of the ion analyser 207 by a pressure differentialcaused by the vacuum chamber 208 being at a lower pressure than theinlets to the tubes 202, 203. The aerosol particles 201 may encounterthe molecules of matrix compound 204 in, and downstream of, the regionthat the sample transfer tube 202 intersects with the matrixintroduction conduit 203. The aerosol particles 201 intermix with thematrix 204 so as to form aerosol particles containing matrix molecules205, in which both the molecular constituents of the aerosol sample 201and the matrix compound 204 are present. The matrix molecules 204 may bein excess compared to the molecular constituents of aerosol sample 201.

The particles 205 may exit the sample transfer tube 202 and pass intothe inlet 206 of the ion analyser 207. The particles 205 then enter intothe decreased pressure region 208 and gain substantial linear velocitydue to the adiabatic expansion of gas entering the vacuum region 208from the sample transfer tube 202 and due to the associated free jetformation. The accelerated particles 205 may impact on the collisionsurface 209, where the impact event fragments the particles 205, leadingto the eventual formation of gas phase ions 210 of the molecularconstituents of the aerosol sample 201 and the formation of matrixmolecules 211. The collision surface 209 may be controlled andmaintained at a temperature that is substantially higher than theambient temperature.

The matrix 204 includes a solvent for the analyte 201, such that theanalyte 201 dissolves by the matrix 204, thereby eliminatingintermolecular bonding between the analyte molecules 201. As such, whenthe dissolved analyte 205 is then collided with the collision surface209, the dissolved analyte 205 will fragment into droplets and any givendroplet is likely to contain fewer analyte molecules than it would ifthe matrix were not present. This in turn leads to a more efficientgeneration of analyte ions 210 when the matrix in each droplet isevaporated. The matrix may include a solvent for said aerosol, smoke orvapour or analyte therein; an organic solvent; a volatile compound;polar molecules; water; one or more alcohols; methanol; ethanol;isopropanol; acetone; acetonitrile; 1-butanol; tetrahydrofuran; ethylacetate; ethylene glycol; dimethyl sulfoxide; an aldehyde; a ketone;non-polar molecules; hexane; chloroform; or (xxii) propanol. Isopropanolis of particular interest.

The matrix molecules 211 may freely diffuse into the vacuum. Incontrast, the gas phase ions 210 of the molecular constituents of theaerosol sample 201 may be transferred by the ion optics 212 to ananalysis region (not shown) of the ion analyser 207. The ions 210 may beguided to the analysis region by applying voltages to the ion optics212.

The ion optics 212 may be a StepWave® ion guide. The collision surfacemay be positioned along and adjacent to the central axis of the largeopening of a StepWave® ion guide. As will be understood by those skilledin the art, a StepWave® ion guide comprises two conjoined ion tunnel ionguides. Each ion guide comprises a plurality of ring or other electrodeswherein ions pass through the central aperture provided by the ring orother electrodes. Ions enter a first of the ion guides, along with anyneutrals that may be present, and travel through the first ion guide.Ions are then directed orthogonally into a second of the ion guides andare transmitted therethrough. Transient DC voltages or potentials areapplied to the electrodes to drive the ions through them. The StepWave®ion guide is based on stacked ring ion guide technology and is designedto maximise ion transmission from the source to the mass analyser. Thedevice allows for the active removal of neutral contaminants, since theneutrals are not directed orthogonally into the second ion guide,thereby providing an enhancement to overall signal to noise. The designenables the efficient capture of the diffuse ion cloud entering a firstlower stage which is then may focused into an upper ion guide fortransfer to the ion analyser. The ions are then analysed by the ionanalyser, which may comprise a mass spectrometer or an ion mobilityspectrometer, or a combination of the two. As a result of the analysis,chemical information about the sample 201 may be obtained.

A liquid trap or separator may be provided between the first device(e.g., forceps 2) and the analyser, which captures or discards undesiredliquids that are aspirated by the probe whilst may allowing the smoke,aerosol or vapour itself to pass relatively uninhibited to the massspectrometer. This prevents undesired liquid from reaching the analyserwithout affecting the measurement of the smoke, aerosol or vapour. Theliquid trap or separator may be arranged to capture the liquid for laterdisposal.

As described above, although embodiments have been described in whichREIMS is used to generate the smoke, aerosol or vapour for analysis,other ambient ionisation techniques may be used such as, for example,Desorption Electrospray Ionisation (“DESI”).

Desorption Electrospray Ionisation (“DESI”)

Desorption Electrospray Ionisation (“DESI”) has also been found to be aparticularly useful and convenient method for the real time rapid anddirect analysis of biological material, e.g., a cell population. DESItechniques allow direct and fast analysis of surfaces without the needfor prior sample preparation. The technique will now be described inmore detail with reference to FIGS. 1E-1F.

As shown in FIGS. 1E-1F, the DESI technique is an ambient ionisationmethod that involves directing a spray of (primary) electrically chargeddroplets 301 onto a target 304. The electrospray mist is pneumaticallydirected at the target 304 by a sprayer 300 where subsequent splashed(secondary) droplets 305 carry desorbed ionised analytes (e.g. desorbedlipid ions). The sprayer 300 may be supplied with a solvent 306, a gas307 (such as nitrogen) and a voltage from a high voltage source 308.After ionisation, the ions travel through air into an atmosphericpressure interface 309 of a mass spectrometer and/or mass analyser (notshown), e.g. via a transfer capillary 310. The ions may be analysed bythe method described in relation to FIG. 1D, or by other methods. Forexample, the transfer capillary 310 of FIG. 1E may correspond to thesample transfer tube 202 in FIG. 1D. The transfer capillary 310 may beheated, e.g., to a temperature up to 500° C.

The DESI technique allows, for example, direct analysis of biologicalmaterials, such as a cell population, e.g., without requiring anyadvance sample preparation for the analysis.

General Methods of the Invention

Various embodiments of the present disclosure relate generally to theapplication of mass spectrometry and/or ion mobility spectrometry to theanalysis of a target in vitro or ex vivo cell population and/or mediumderived therefrom. Various embodiments of the invention also providemethods of drug discovery, testing, and/or production.

A number of optional features will be described in greater detail below.The invention provides method of analysis using mass spectrometry and/orion mobility spectrometry comprising:

-   -   (a) using a first device to generate smoke, aerosol or vapour        from a target in vitro or ex vivo cell population and/or medium        derived therefrom; and    -   (b) mass analysing and/or ion mobility analysing said smoke,        aerosol or vapour, ions derived therefrom, in order to obtain        spectrometric data; and optionally    -   (c) analysing said spectrometric data in order to identify        and/or characterise the target cell population or one or more        cells and/or compounds present in said target cell population        and/or medium derived therefrom.

Thus, optionally, the method may comprise a step of identifying and/orcharacterising the target cell population or one or more cells and/orcompounds present in said target cell population and/or medium derivedtherefrom on the basis of said spectrometric data. It should beunderstood that any reference herein to “analysing” a target is intendedto mean that the target is analysed on the basis of the spectrometricdata. Thus, for example, by an expression, such as, “analysingspectrometric data in order to determine whether a cell populationsuffers from an infection” is meant that whether a cell populationsuffers from an infection is determined based upon the spectrometricdata.

The method may optionally be a method of screening, e.g., for thepurpose of drug development. Thus, optionally, the method may comprise astep of analysing the response of a cell population to a test agent orcondition.

Optionally, the identity of a cell population or one or more cell typespresent therein may be analysed. Optionally, the infection of a cellpopulation may be analysed. Optionally, the homogeneity and/orheterogeneity of a cell population may be analysed. Optionally, thegenotype and/or phenotype of a cell population or one or more cell typespresent therein may be analysed. Optionally, the state of a cellpopulation or one or more cell types present therein may be analysed.Optionally, a process involving a cell population or one or more celltypes present therein may be analysed. Optionally, the effect ofmanipulating the genotype and/or phenotype of a cell population or oneor more cell types present therein may be analysed. Optionally, theeffect of manipulating the environmental conditions of a cell populationmay be analysed. Optionally, the method may be used to distinguishbetween 2 or more different cell types within a cell population.Optionally, the disease state of a cell population may be analysed.Optionally, the effect of a substance on a cell population may beanalysed. Optionally, the utilisation, production and/or breakdown of asubstance may be analysed. Optionally, a plurality of cell populationsmay be analysed to analyse their ability to utilise, produce and/orbreak down a substance. Thus, optionally, a plurality of cellpopulations may be screened to analyse their productivity or efficiencywith respect to the production, breakdown and/or utilisation of asubstance. Optionally, the viability of a cell population may beanalysed.

In one aspect, the method may be a method of analysing a disease, adiseased cell, and/or a biomarker of a disease. Thus, the method mayoptionally comprise a step of analysing a disease, a diseased cell,and/or a biomarker of a disease.

The method may be a method of, or of obtaining information relevant to:

(i) diagnosing a disease;

(ii) monitoring the progression or development of a disease;

(iii) disease prognosis;

(iv) predicting the likelihood of a disease responding to treatment;

(v) monitoring the response of a disease to treatment; and/or

(vi) stratifying subjects;

Thus, the method may optionally comprise a step of

(i) diagnosing a disease;

(ii) monitoring the progression or development of a disease;

(iii) disease prognosis;

(iv) predicting the likelihood of a disease responding to treatment;

(v) monitoring the response of a disease to treatment; and/or

(vi) stratifying subjects.

The method may be a method of, or of obtaining information relevant to,predicting the viability of a cell population in terms of its long termviability, robustness and/or efficiency.

Details of suitable diseases are provided elsewhere herein.

In one aspect, the method may be a method of analysing a microbe, amicrobial interaction, and/or a microbial biomarker. Thus, the methodmay optionally comprise a step of analysing a microbe, a microbialinteraction, and/or a microbial biomarker.

In one aspect, the method may be a method of analysing the genotypeand/or phenotype of a cell. Thus, the method may optionally comprise astep of analysing the genotype and/or phenotype of a cell.

In one aspect, the method may be a method of treatment. Thus, the methodmay optionally comprise a step of administering a therapeuticallyeffective amount of a therapeutic agent to a subject in need thereof.

In one aspect, the method may be a method of analysing a compound. Thus,the method may optionally comprise a step of analysing a compound and/ora biomarker for a compound.

Optional features of any of these methods are discussed below. Thus,unless otherwise stated, any reference to “a method” or “the method” isintended to be a reference to any of the methods of the invention listedherein. It is explicitly intended that any of these features may bepresent in any combination in any of these methods.

The skilled person will appreciate that any of the methods providedherein may optionally be combined with one or more of the other methodsprovided herein and/or with one or more further methods.

For example, provided is a method which is a combination of two or more,e.g., three or more, four or more or five or more of the methodsdisclosed herein.

Target Cell Populations

The method may be carried out on a target cell population and/or mediumderived therefrom. By “cell population” is meant an in vitro or ex vivocollection of cells. Thus, the cell population may be referred to as an“in vitro or ex vivo cell population”. Thus, the term “cell population”does not extend to entire organisms, such as animals or humans.

The cell population may optionally, e.g., be primary cell culture, asecondary cell culture, a cell line, a xenograft-derived cell populationand/or an organoid.

A “primary cell culture” is a culture of cells that were dissociatedfrom the parental tissue using mechanical or enzymatic methods. Aprimary cell culture may, e.g., be an adherent cell culture or a cellsuspension culture.

A “secondary cell culture” is a culture of cells which may e.g., bederived from a primary cell culture via multiple cell passages.

A “cell line” is a cell population which is typically uniform and whichcan be cultured in vitro for several passages. A cell line may, e.g., bederived from a secondary cell culture, from a tumour, and/or from anembryo. A cell line may optionally be “immortalised”, in which case itcan be cultured indefinitely. Immortalised cell lines typically have oneor more mutations that override the cells' natural growth controls.

A “xenograft-derived cell population” is a cell population derived froma xenograft. A “xenograft” refers to cellular material, such as tissue,that originated from a first subject and was inserted into a secondsubject. Optionally, the xenograft may comprise or consist of tumourcells. For example, cells or tissue obtained from a human tumour may bexenografted into a host animal. Optionally, cells may be obtained from axenograft to establish a xenograft-derived cell population.

Further details of cell lines and organoids are provided elsewhereherein. A cell population is a plurality of cells, which may optionallycomprise extracellular compounds and/or extracellular medium. Thus,unless a “washed” cell population is used, the term “cell population”refers to cells and extracellular compounds within the cell population.Any reference herein to analysing a compound “in” a cell populationshould be understood to mean that the compound may be intracellularand/or extracellular.

As mentioned above, the method may be carried on medium derived from acell population. By this is meant medium that was in contact with thecell population. Optionally, the medium derived from a cell populationmay be the medium in which the cell population was cultured, i.e. theextracellular medium, for a suitable period of time, e.g. at least 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23, 24, 30, 45, 50, or 55 minutes, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours and/or 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23, or 24 days. During culture, cells may release one or more substancesinto the extracellular medium, so the medium may be referred to as“conditioned medium”.

The extracellular medium may optionally comprise the cell population, orcomprise one or more cells, or it may be cell-free. Cell-freeextracellular medium may be prepared by removing the cells, e.g.,through filtration and/or centrifugation as described elsewhere herein.

Thus, the target may optionally be medium derived from a cell populationand the method may optionally comprise analysing the medium to analyseone or more compounds.

For example, the medium may be analysed to analyse the utilisation,production and/or breakdown of a substance.

Any references herein to carrying out a method on a cell populationshould be understood to encompass carrying out the method on the cellpopulation and/or on medium derived from the cell population.

The cell population may optionally be a human or non-human animal cellpopulation. Optionally, it may be mammalian, e.g., originate from alivestock, domestic or laboratory animal, e.g., be a rodent cellpopulation. Optionally, it may be murine, guinea pig, hamster, rat,goat, pig, cat, dog, sheep, rabbit, cow, horse, alpaca, ferret, fowl,buffalo, and/or monkey.

The cell population may comprise or consist of one or more differentcell types.

Unless otherwise specified herein, any reference herein to a “cell”should be understood to be a reference to a cell that is part of a cellpopulation.

Optionally, the cell population may comprise or consist of adult,embryonic, and/or foetal cells, e.g., human embryonic cells or humanadult cells.

Optionally, the cell population may comprise or consist of stem cellsand/or differentiated cells. Optionally, stem cells may be totipotentstem cells, pluripotent stem cells, multipotent stem cells, and/oroligopotent stem cells.

Optionally, the cell population may comprise or consist of fibroblasts,epithelial cells, lymphocytes and/or macrophages.

Optionally, the cell population may comprise or consist of cellsoriginating from, or having the characteristics of, cells of adrenalgland tissue, appendix tissue, bladder tissue, bone, bowel tissue, braintissue, breast tissue, bronchi, ear tissue, oesophagus tissue, eyetissue, endometrioid tissue, gall bladder tissue, genital tissue, hearttissue, hypothalamus tissue, kidney tissue, large intestine tissue,intestinal tissue, larynx tissue, liver tissue, lung tissue, lymphnodes, mouth tissue, nose tissue, pancreatic tissue, parathyroid glandtissue, pituitary gland tissue, prostate tissue, rectal tissue, salivarygland tissue, skeletal muscle tissue, skin tissue, small intestinetissue, spinal cord, spleen tissue, stomach tissue, thymus gland tissue,trachea tissue, thyroid tissue, ureter tissue, urethra tissue, softand/or connective tissue, peritoneal tissue, blood vessel tissue and/orfat tissue; grade I, grade II, grade Ill or grade IV cancerous tissue;metastatic cancerous tissue; mixed grade cancerous tissue; a sub-gradecancerous tissue; healthy or normal tissue; or cancerous or abnormaltissue.

Optionally, the cell population may comprise or consist of adipocytes,endothelial cells, epidermal cells, epithelial cells, fibroblasts, glialcells, keratinocytes, mesenchymal cells, myoblasts, neuronal cells,squamous cells, stromal cells, and/or trophoblasts.

Optionally, the cell population may comprise or consist of cells havingthe identity of any one of the cell lines listed below.

Lung cancer—NCI-H23; NCI-H226; NCI-H322M; NCI-H460; NCI-H522; A549/ATCC;EK VX; HOP-18; HOP-62; HOP-92; LXFL 529; DMS 114; DMS 273; Renalcancer-UO-31; SN12C; A498; CAKI-1; RXF 393; RXF 631; ACHN; 786-0; TK-10;

Colon cancer—HT29; HCC-2998; HCT-116; SW-620; COLO 205; DLD-1; HCT-15;KM12; KM20L2;

Melanoma—LOX IMVI; MALME-3M; SK-MEL-2; SK-MEL-5; SK-MEL-28; M19-MEL;UACC-62; USACC-257; M14;

Central nervous system (CNS) cancer—SNB-19 (glioblastoma); SNB-75;SNB-78; U251; SF-268; SF-295; SF-539; XF 498;

Ovarian cancer—OVCAR-3; OVCAR-4; OVCAR-5; OVCAR-8; IGR-OV-1; SK-OV-3;

Leukemia—CCRF-CEM; K-562; MOLT-4; HL-60; RPMI-8226; SR.

Optionally, the cell population may comprise or consist of healthyand/or diseased cells. Diseased cells may optionally have a diseaseselected from any of the diseases listed elsewhere herein, optionallycancer.

Optionally, the cell population may comprise or consist of one or moreof the following cell types: 3T3, Chinese hamster ovary (CHO), BHK,HEK293 (embryonic kidney cells), SKNBE2 (neuroblastoma), SW480 (coloncarcinoma), Hela (cervical adenocarcinoma), PC3M, HOP62, T24, MES_SA(uterine sarcoma) and/or HepG2.

Optionally, the cell population may be mutant and/or transgenic.

Optionally, the cell population may be immortalised.

Optionally, the cell population may have, or be/have been geneticallymanipulated to have, one or more properties selected from auxotrophy,production of a desired compound, and/or secretion of a desiredcompound. Optionally, the cell population may be, or have been,genetically manipulated, e.g., be transgenic and/or have a knock-outgenotype and/or phenotype.

Details of genetic manipulation and cell population properties areprovided elsewhere herein.

Optionally, the method may comprise the analysis of one or more isogeniccell populations.

Any reference herein to the analysis of a “cell population” should beunderstood to mean that the entire cell population, or a sample thereof,may be analysed.

Optionally, a cell population may be optimised for analysis via a methodprovided herein, for example, for analysis wherein the ambientionisation source is a REIMS ionisation source. In this regard, a cellpopulation may optionally be optimised in that it expresses a specificreceptor. The cell population may naturally express such a receptor, orit may have been genetically manipulated to express such a receptor. Theoptimised cell population may optionally be administered to a subject.This may facilitate subsequent analysis of the subject via a methodprovided herein.

The method may optionally be carried out on an entire cell population,or on a sample thereof, or on region thereof, particularly if the cellpopulation is an organoid. It should be understood that any referenceherein to a “cell population” may optionally be a “sample of a cellpopulation”. The method may optionally be carried out on a mediumderived from a cell population, or on a sample thereof. Prior toperformance of the method provided herein, the cell population or samplethereof, or medium or sample thereof, may optionally be dried, collectedwith a swab, and/or dispensed onto an absorbent carrier, e.g., a filteror paper. Optionally, the cell population or sample thereof may beprovided as a pellet. A pellet may be prepared, e.g., by centrifuging afluid containing the cell population, e.g., a liquid culture, at asuitable force and for a suitable time to sediment any cells, largestructures and/or macromolecules to form a pellet. The remainder of thefluid, i.e. the supernatant, may then be discarded, e.g., by tipping itout, or via aspiration.

The pellet may be sampled straight from the bottom of the centrifugetube, or it may be sampled from a support onto which it has beentransferred. For example, prior to sampling, a pellet may optionally betransferred onto a glass or plastic support, such as a slide, or onto aswab, such as, a cotton swab.

The cell population, e.g., a pellet, may optionally be subjected to oneor more washing steps, e.g., to remove the culture medium. Washing maybe performed with a suitable buffer. Thus, the method may optionally beperformed on a washed cell population.

The method may optionally involve the analysis of one or more differenttargets. Optionally, 2 or more targets from different cell populations,from different locations within an organoid, and/or from the same cellpopulation at different time points, may be analysed. Optionally, thetargets may be at 2 or more different locations, e.g., at 2 or morelocations in an organoid.

Optionally, a target may be at one or more locations of an organoidknown or suspected to be healthy; and one or more locations of anorganoid known or suspected to be diseased.

Optionally, the method may involve the analysis of 2 or more locationsof a target. Optionally, distinct locations of a target may be analysed,e.g., a series of points may be sampled, optionally with or withoutspatial encoding information for imaging purposes.

The method may optionally be carried out on a target that is native. By“native” is meant that the target has not been modified prior toperforming the method provided herein. In particular, the target may benative in that the cell population is not subjected to a step of lysisor extraction, e.g., lipid extraction, prior to performance of themethod provided herein. Thus, a target may be native in that all orsubstantially all of the cells in the cell population are intact. Thus,by native is meant that the target has not been chemically or physicallymodified and is thus chemically and physically native. Optionally, thetarget may be chemically native, i.e. it may be chemically unmodified,meaning that it has not been contacted with a chemical agent so as tochange its chemistry. Contacting a target with a matrix is an example ofa chemical modification.

Optionally, the target may be physically native, i.e. it may bephysically unmodified, meaning that it has not been modified physically.Freezing and/or thawing are examples of physical modifications. Theskilled person will appreciate that although physical actions, such as,freezing, may affect a specimen's chemistry, for the purpose of thisinvention such an action is not considered to be a chemicalmodification.

Thus, optionally the target may be chemically native, but not physicallynative, e.g., because it has been frozen.

Optionally, the target may be frozen, previously frozen and then thawed,and/or otherwise prepared. Optionally, the method may be carried out ona target that has not undergone a step of preparation specifically forthe purpose of mass spectrometry analysis.

The target may not have been contacted with a solvent, or a solventother than water, prior to generating the smoke, aerosol or vapour fromthe target.

Additionally, or alternatively, the target may not be contacted with amatrix prior to generating the smoke, aerosol or vapour from the target.For example, the target may not be contacted with a MALDI matrix orother matrix for assisting ionisation of material in the target.

Alternatively, the target may be contacted with a matrix, e.g., a MALDImatrix or other matrix for assisting ionisation of material in thetarget. The matrix may be added to said aerosol, smoke or vapour priorto the aerosol, smoke or vapour being ionised and/or impacting upon thecollision surface.

The method may optionally be carried out on a target that has beenprepared for a particular mass spectrometry analysis; and/or that hasbeen prepared for any of the analytical methods mentioned elsewhereherein.

Target preparation (for any of the methods of the invention and/or anyof the analytical methods disclosed herein) may optionally involve oneor more of the following.

The cell population may optionally be deposited on a solid surface, suchas, a glass or plastic slide.

The target may optionally be fixed chemically, e.g., to preserve cellsfrom degradation, and to maintain the structure of the cell and ofsub-cellular components such as cell organelles, e.g., nucleus,endoplasmic reticulum, and/or mitochondria. The fixative may, forexample, be 10% neutral buffered formalin.

Freezing may optionally be performed, e.g., by contacting the cellpopulation with a suitable cooling medium, such as, dry ice, liquidnitrogen, or an agent that has been cooled in dry ice or liquidnitrogen, e.g., isopentane (2-methyl butane). Frozen cell populationsmay optionally be stored at, e.g., between about −80 and −4 degreesCelsius, e.g., at −70 or −20 degrees Celcius.

The cell population may optionally be stained and/or labelled.

The term “sampling” is used herein to refer to the use of a device togenerate smoke, vapour or aerosol from a target.

Any of the methods may optionally include automatic sampling, which mayoptionally be carried out using, e.g., a REIMS device. Any of themethods may optionally comprise using a disposable sampling tip.

Analysis of Identity or Authenticity

Human error and/or other circumstances can lead to misidentification,mislabelling, mix-ups, and the like, of cell populations. Thus, theidentity of a particular cell population may be unknown, uncertainand/or unconfirmed. Optionally, the method may be used to identify acell population, and/or to confirm the identity of a cell population.

By “identifying” a cell population is meant that at least someinformation about the type(s) of cells present in the cell population isobtained. This may optionally be the determination of the identity,and/or the confirmation of the identity of one or more cell types in thecell population. Confirming the identity of a cell population may alsobe referred to as confirming the authenticity of a cell population.

Thus, optionally the method may be performed on a cell population whoseidentity is unknown, to determine the identity of the cell population.

Optionally, the method may be performed on a cell population suspectedof having a particular identity, to confirm or refute the identity ofthe cell population.

Optionally, the method may be performed on a cell population in need ofauthentication, to confirm the authenticity of the cell population.

Optionally, the cell population may be identified as comprising orconsisting of adult, embryonic, and/or foetal cells.

Optionally, the cell population may be identified as comprising orconsisting of stem cells and/or differentiated cells. Optionally, stemcells may be totipotent stem cells, pluripotent stem cells, multipotentstem cells, and/or oligopotent stem cells.

Optionally, the cell population may be identified as comprising orconsisting of fibroblasts, epithelial cells, lymphocytes and/ormacrophages.

Optionally, the cell population may be identified as comprising orconsisting of cells originating from, or having the characteristics of,cells of adrenal gland tissue, appendix tissue, bladder tissue, bone,bowel tissue, brain tissue, breast tissue, bronchi, ear tissue,oesophagus tissue, eye tissue, endometrioid tissue, gall bladder tissue,genital tissue, heart tissue, hypothalamus tissue, kidney tissue, largeintestine tissue, intestinal tissue, larynx tissue, liver tissue, lungtissue, lymph nodes, mouth tissue, nose tissue, pancreatic tissue,parathyroid gland tissue, pituitary gland tissue, prostate tissue,rectal tissue, salivary gland tissue, skeletal muscle tissue, skintissue, small intestine tissue, spinal cord, spleen tissue, stomachtissue, thymus gland tissue, trachea tissue, thyroid tissue, uretertissue, urethra tissue, soft and connective tissue, peritoneal tissue,blood vessel tissue and/or fat tissue; grade I, grade II, grade Ill orgrade IV cancerous tissue; metastatic cancerous tissue; mixed gradecancerous tissue; a sub-grade cancerous tissue; healthy or normaltissue; or cancerous or abnormal tissue.

Optionally, the cell population may be identified as comprising orconsisting of adipocytes, endothelial cells, epidermal cells, epithelialcells, fibroblasts, glial cells, keratinocytes, mesenchymal cells,myoblasts, neuronal cells, squamous cells, stromal cells, and/ortrophoblasts.

Optionally, the cell population may be identified as comprising orconsisting of any one of the cell lines listed elsewhere herein, e.g.,having the identity of any one of the cell lines listed elsewhereherein.

Optionally, the cell population may be identified as comprising orconsisting of healthy and/or diseased cells, wherein diseased cells mayoptionally have a disease selected from any of the diseases listedelsewhere herein.

Optionally, the cell population may be identified as comprising orconsisting of mutant and/or transgenic cells.

Optionally, the cell population may be analysed (i) to confirm theidentity or authenticity of said cell population; (ii) to detect amutation in said cell population; and/or (iii) to detect an undesiredvariation in said cell population.

Analysis of Infection

A cell population may be at risk of infection with another cell typeand/or a microbe. “Infection”, with another cell type and/or microbe mayalso be referred to herein as “contamination”. A particularly high riskis contamination with Mycoplasma.

Optionally, the method may be used to analyse whether infection ispresent in a cell population.

Optionally, the method may be used (i) to determine whether or not saidcell population suffers from an infection; (ii) to determine whether ornot said cell population is infection free; (iii) to determine whetheror not said cell population has been cured of an infection; (iv) todetermine the progression or stage of an infection of a cell population;or (v) to determine the progression or stage of a treatment for aninfection of a cell population.

Optionally, if infection is determined, the infecting cell type and/ormicrobe may be identified.

Optionally, if infection is determined, the method may involve a step oftreating/removing the infection, e.g., through contacting the cellpopulation with an appropriate substance that is effective atselectively killing, or inhibiting the growth of, the infecting celltype and/or microbe. For example, if infection with Mycoplasma isdetermined, a suitable antibiotic may be used.

Analysis of Phenotype, Genotype and/or Homogeneity

A major area of on-going medical research is to gain greaterunderstanding of the complex interactions between the 50,000 to 100,000genes which make up the human genome in terms of interactions betweendifferent genes and interactions between genes and the environment. Inparticular, it is desired to gain a greater understanding of therelationship between a particular gene and a particular phenotype orobserved characteristic.

There are several different types of genetic disease.

Firstly, there are single gene disorders which can be traced throughfamilies. A single gene disorder is the result of a single mutated gene.Over 4000 human diseases are caused by single gene defects and in manycases it has been possible to isolate the genes involved and todetermine the types of mutation which underlie these conditions.

Secondly, there are polygenic or multigenic diseases which appear tohave a genetic component but which do not follow any simple pattern ofinheritance. These diseases reflect the effect of varying geneticsusceptibility to a variety of different environmental agents.

Thirdly, there are genetic diseases which result from changes in thestructure or number of our chromosomes.

Genetic mutations may alter the structure of a protein, e.g., by codingfor a different amino acid, and/or by resulting in a shortened orelongated protein. Genetic mutations may alternatively or in additionresult in a reduced output or absence of a gene product.

One of the best examples of genetic mutation comes from inheriteddisorders of haemoglobin (Hb). The structure of human haemoglobinchanges during embryonic, fetal and adult life. All normal haemoglobinsare tetramers of two pairs of dissimilar globin chains. Adult and fetalhaemoglobins have α chains combined with β (HBA α₂β₂) or γ chains (HbFα₂γ₂). Over 400 structural haemoglobin variants has been identified butmany of these cause no clinical disability.

However, other variants due to amino acid substitution alter thestability or function of the haemoglobin molecule which thereby resultsin a disease phenotype. For example, the substitution of glutamic acidfor valine in the sixth position of the p chain causes the haemoglobinmolecules to form linear stacks in the deoxy configuration which in turncases the red cells to assume a sickled configuration. The resultingdisease (sickle cell anaemia) results in chronic anaemia and tissuedamage due to blockage of the microcirculation with sickled red cells.Other mutations affect oxygen transport or bind oxygen more avidly thannormal causing oxygen starvation. Although the different phenotypesassociated with these different genetic mutations are broadly the same,there may be considerable individual variation associated with anidentical mutation. For example, sick cell anaemia can vary from beinglife-threatening to being symptomless and causing little disability.

Cystic fibrosis (CF) is another example of a monogenic disease whichshows remarkable phenotypic variability.

The term “phenotype” is used to refer to the physical and/or biochemicalcharacteristics of a cell whereas the term “genotype” is used to referto the genetic constitution of a cell.

The term “phenotype” may be used to refer to a collection of a cell'sphysical and/or biochemical characteristics, which may optionally be thecollection of all of the cell's physical and/or biochemicalcharacteristics; and/or to refer to one or more of a cell's physicaland/or biochemical characteristics. For example, a cell may be referredto as having the phenotype of a specific cell type, e.g., a breast cell,and/or as having the phenotype of expressing a specific protein, e.g., areceptor, e.g., HER2 (human epidermal growth factor receptor 2).

The term “genotype” may be used to refer to genetic information, whichmay include genes, regulatory elements and/or junk DNA. The term“genotype” may be used to refer to a collection of a cell's geneticinformation, which may optionally be the collection of all of the cell'sgenetic information; and/or to refer to one or more of a cell's geneticinformation. For example, a cell may be referred to as having thegenotype of a specific cell type, e.g., a breast cell, and/or as havingthe genotype of encoding a specific protein, e.g., a receptor, e.g.,HER2 (human epidermal growth factor).

The genotype of a cell may or may not affect its phenotype, as explainedbelow.

The relationship between a genotype and a phenotype may bestraightforward. For example, if a cell includes a functional geneencoding a particular protein, such as HER2, then it will typically bephenotypically HER2-positive, i.e. have the HER2 protein on its surface,whereas if a cell lacks a functional HER2 gene, then it will have aHER2-negative phenotype.

A mutant genotype may result in a mutant phenotype. For example, if amutation destroys the function of a gene, then the loss of the functionof that gene may result in a mutant phenotype. However, factors such asgenetic redundancy may prevent a genotypic trait to result in acorresponding phenotypic trait. For example, human cells typically have2 copies of each gene, one from each parent. Talking the example of agenetic disease, a cell may comprise 1 mutant (diseased) copy of a geneand one non-mutant (healthy) copy of the gene, which may or may notresult in a mutant (diseased) phenotype, depending on whether the mutantgene is recessive or dominant. Recessive genes do not, or notsignificantly, affect a cell's phenotype, whereas dominant genes doaffect a cell's phenotype.

It must also be borne in mind that many genotypic changes may have nophenotypic effect, e.g., because they are in junk DNA, i.e. DNA whichseems to serve no sequence-dependent purpose, or because they are silentmutations, i.e. mutations which do not change the coding information ofthe DNA because of the redundancy of the genetic code.

The phenotype of a cell may be determined by its genotype in that a cellrequires genetic information to carry out cellular processes and anyparticular protein may only be generated within a cell if the cellcontains the relevant genetic information. However, the phenotype of acell may also be affected by environmental factors and/or stresses, suchas, temperature, nutrient and/or mineral availability, toxins and thelike. Such factors may influence how the genetic information is used,e.g., which genes are expressed and/or at which level. Environmentalfactors and/or stresses may also influence other characteristics of acell, e.g., heat may make membranes more fluid.

If a functional transgene is inserted into a cell at the correct genomicposition, then this may result in a corresponding phenotype

The insertion of a transgene may affect a cell's phenotype, but analtered phenotype may optionally only be observed under the appropriateenvironmental conditions. For example, the insertion of a transgeneencoding a protein involved in a synthesis of a particular substancewill only result in cells that produce that substance if the cells areprovided with the required starting materials.

Optionally, the method may involve the analysis of the phenotype and/orgenotype of a cell population.

The genotype and/or phenotype of cell population may be manipulated,e.g., to analyse a cellular process, to analyse a disease, such ascancer, to make a cell population more suitable for drug screeningand/or production, and the like. Optionally, the method may involve theanalysis of the effect of such a genotype and/or phenotype manipulationon the cell population, e.g., on the genotype and/or phenotype of thecell population.

The method may optionally be used to analyse a cell population aftermutagenesis. Conventional methods for confirming whether or not a cellhas been mutated can be difficult and/or time consuming. Optionally, themethod may be used to analyse whether a cell has been mutated. Amutation may, e.g., be the introduction of a new gene, the silencing ofa gene, an alteration in the expression of a gene, or give rise to analtered protein. Silencing may, e.g., be achieved via gene knock-out.

Optionally, the method may be used to analyse the effect of mutagenesison a cell population, e.g., on the genotype and/or phenotype of a cellpopulation.

Optionally, the method may analyse a cell population at 2 or more timepoints, e.g., before and after mutagenesis, and/or at 2 or more timepoints after mutagenesis.

Optionally, the cell population may be homogeneous or heterogeneous. By“homogeneous”, “homogeneity” and derivatives of these terms is meantthat the population is uniform, and by “heterogeneous”, “heterogeneity”and derivatives of these terms is meant that the population isnon-uniform.

By “degree of homogeneity” or “degree of heterogeneity” is meant theextent to which a cell population is homogeneous or heterogeneous, whichmay be expressed as a percentage. For example, a cell population may beconsidered to have a high degree of homogeneity if at least 60, 70, 75,80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of the cells arehomogenous. A cell population may be considered to have a high degree ofheterogeneity if at least 40, 45, 50, 55, 60, 70, 75, 80, 85, 90, 91,92, 93, 94, 95, 96, 97, 98, 99 or 100% of the cells are heterogeneous.

The homogeneity and/or heterogeneity may be with respect to one or moregenotypic and/or phenotypic features, e.g., at least 1, 2, 3, 4, 5, 6,7, 8, 9 or 10 genotypic and/or phenotypic features, optionally withrespect to the cells' entire genotype and/or phenotype.

Optionally, the method may involve the analysis of the degree ofhomogeneity and/or heterogeneity of the cell population.

During culture of a cell population, cells may grow and replicate.Replication may involve self-renewal, i.e. the production of a daughtercell having the same genotype and/or phenotype as the mother cell,and/or differentiation, i.e. the production of a daughter cell having adifferent genotype and/or phenotype compared to the mother cell.

Alternatively or in addition, cells may acquire one or more mutationsand thus acquire a different genotype, which may manifest itself as adifferent phenotype.

In a heterogeneous cell population, one type may grow and/or replicatebetter or in a different way to another cell type, and/or one cell typemay become dormant and/or die.

For these and/or other reasons, cell population may become more or lessheterogeneous, so the method may optionally involve monitoring for anychanges in the homogeneity and/or heterogeneity of the cell population.

Optionally, if the degree of homogeneity and/or heterogeneity of thecell population is higher or lower than desired, the method may involvea step of influencing the degree of homogeneity and/or heterogeneity.This may, e.g., involve the adjustment of culture conditions and/or theaddition of a substance, to affect, e.g., the growth and/ordifferentiation rate of one or more of the cell types present in thecell population.

Manipulation of Genotype and/or Phenotype

Optionally, a cell population may be manipulated, e.g., the phenotypeand/or genotype of some or all of the cells that make up the cellpopulation may be manipulated.

The manipulation may optionally involve the exposure of a cellpopulation or a portion thereof to a compound and/or radiation.

The manipulation may optionally be genetic manipulation.

Genetic manipulation may alter one or more genomic region(s) of a cell,which genomic region may be in the coding region of a gene, thenon-coding region of a gene, a regulatory region, e.g., a promoter orenhancer, and/or in a region called “junk” DNA.

Genetic manipulation may optionally involve random mutagenesis. Forexample, cells may be exposed to a mutagen, which may, e.g., be selectedfrom a chemical mutagen and/or radiation.

A compound, which may optionally be a chemical mutagen, may optionallybe selected from, e.g., an alkylating agent, cross-linking agent, and/orpolycyclic aromatic hydrocarbons (PAHs). Alkylating agents act by addingmolecular components to DNA bases, which alters the protein product.Cross-linking agents create covalent bonds with DNA bases, while PAHsare metabolized by the human body into other potentially mutagenicmolecules.

Radiation may optionally be selected from, e.g., light of a suitablewavelength, heat, and/or ionizing radiation. Ionizing radiation canpenetrate cells and create ions in the cell contents. These ions cancause permanent alterations in DNA. Ionizing radiation may optionally beselected from, e.g., x rays, gamma rays, neutrons, electrons (“beta”particles), and/or alpha particles (helium nuclei). Ionizing radiationcan alter the way two strands of DNA interact. It can rearrange entiresections of the chromosomes, altering relatively long stretches of DNA.Light may optionally be, e.g., UV light. This can cause covalent bondsto form between neighbouring thymine bases in the DNA, thereby alteringthe DNA at that location.

Alternatively or in addition to random mutagenesis, genetic manipulationmay optionally involve targeted mutagenesis, which may optionally, e.g.,be the knock-out, alteration, and/or insertion of genetic information. Acell that has been manipulated via targeted mutation may be referred toas a “transformed” cell, particularly if a new gene or gene variant,i.e. a “transgene” has been inserted. Similarly, a cell populationcomprising or consisting of cells that have been manipulated viatargeted mutation may be referred to as a “transformed” cell population.Similarly, an organoid comprising or consisting of cells that have beenmanipulated via targeted mutation may be referred to as a “transformed”organoid.

Mutagenesis may optionally involve, e.g., one or more of the followingtechniques to introduce the desired genetic material, such as atransgene, into a cell: microinjection into the nucleus of a cell; aviral vector, e.g., an adenoviral or lentiviral vector; a liposome;calcium phosphate; a dendrimer; a cationic polymer, such as DEAE-dextranand/or polyethylenimine; sonication; electroporation; magnet-assistedtransfection with magnetic particles; and/or particle bombardment.

Mutagenesis may optionally involve genome editing, e.g., usingprogrammable nucleases, such as zinc-finger nucleases (ZFNs),transcription activator-like effector nucleases (TALENs), and/orclustered regularly interspaced short palindromic repeats(CRISPR)/CRISPR-associated protein 9 (Cas9).

Optionally, the method may involve a step of random and/or targetedmutagenesis, e.g., via any of the methods mentioned herein.

Properties of Cell Populations

The method may be carried out on a cell population having a desiredproperty by selecting an existing cell population with the desiredproperty. Alternatively or in addition, a cell population may bemanipulated to impart a desired property unto the cell population. Themethod may optionally involve the analysis of one or more properties ofa cell population.

Properties of a cell population that may be selected, manipulated,analysed or the like may optionally be selected from any of theproperties listed below.

The cell population may optionally be auxotrophic with respect to one ormore substances. Auxotrophy is the inability, or reduced ability, of acell or organism to synthesize a particular substance required for itsgrowth. An auxotroph is an organism that displays this characteristic;auxotrophic is the corresponding adjective. For example, an auxotrophmay have a deficiency in a metabolic enzyme selected from adeninephosphoribosyl transferase (APRT) and/or dihydrofolate reductase (DHFR).

The cell population may optionally have the ability to produce a desiredsubstance, e.g., a drug. Thus, the cell population may optionally havethe ability to utilise a substance, e.g., to metabolise a substratemolecule to form a desired compound or precursor. Utilisation of a firstsubstance may involve using a first substance as a substrate to producea second substance; using a first substance as a general nutrient;and/or breaking down a first substance.

The cell population may optionally have a high specific productivitywith respect to the production of a desired substance. The cellpopulation may optionally have a high efficiency with respect to theutilisation and/or breakdown of a desired substance. It may optionallycomprise high levels of, or have the ability to generate high levels of,one or more key metabolites linked to energy generation, regulation ofcellular redox potential, and precursors for glycosylation. The methodmay optionally be used to determine whether a lowproductivity/efficiency cell population exhibits a different metabolicprofile than its high productivity/efficiency counterpart. The methodmay optionally be used to analyse the specific productivity potential,and/or or efficiency with respect to the utilisation and/or breakdown ofa desired substance, of a cell population.

The cell population may optionally have the ability to secrete aproduced substance.

The cell population may optionally have the ability to replicaterapidly.

Lactate consumption may help control pH levels, which in turn mayimprove cell viability. The cell population may optionally have, or havethe ability of, a high lactate consumption.

The method may optionally be used to analyse the metabolome, lipidomeand/or proteome of a cell population.

The metabolome is a collection of some or all of the small-moleculemetabolites present in a cell. The lipidome is a collection of some ofall of the lipids present in a cell. The proteome is a collection ofsome of all of the proteins present in a cell. Although many proteinsand some metabolites may not necessarily be analysed directly via themethod provided herein, they may optionally be analysed indirectly, byanalysing an indirect biomarker therefor.

The method may optionally involve the analysis of the state of a cellpopulation or one or more cell types present therein. By “state” ismeant the condition of a cell population or one or more cell typespresent therein, which may, e.g., be healthy and growing; healthy andnot growing; stressed and growing; stressed and not growing; dying; ordead.

The method may optionally involve the analysis of the viability of acell population. By “viability” is meant the minimum length of time thatthe cell population will continue to live. The viability may also bereferred to as the “robustness”, as robust cell populations are likelyto live longer than non-robust cell populations.

The method may optionally involve the analysis of a cellular process. Acellular process may, e.g., be the production of a substance; theutilisation of a nutrient; a response to exposure to a substance; aresponse to exposure to an environmental condition and the like.

The method may optionally involve the identification of a spectrometricbiomarker for a cell type, phenotype, genotype and/or property. Asmentioned above, the identity and characteristics of many cellpopulations, e.g., cell lines, are known. In particular, data availablefor the NC60 cell lines includes drug sensitivity patterns for more than100,000 compounds and natural products, global protein and geneexpression data and common mutations associated with cancer. The methodprovided herein allows the identification of spectrometric biomarkers ofthese cell types or cell characteristics. Thus, the method may be used,e.g., to correlate a characteristic with spectrometric data, e.g., aspectrometric biomarker. The characteristic may, e.g., be a genotypeand/or a phenotype, e.g., the sensitivity to a particular substance. Forexample, as discussed below, correlations between REIMS spectralfeatures and gene expression profiles were identified by the inventorand are exemplified in case of fads2 and ugcg genes.

Drug Discovery and Screening of Agents, e.g., Cytotoxic Agents

It is known to use cell-based platforms to advance drug discovery, e.g.,anti-cancer drug discovery, and it will be understood by those skilledin the art that cell-based compound screens and bioassays are essentialfor such drug discovery.

It is known to perform non-mass spectrometry high throughputcytotoxicity screening using a panel of cell populations coveringvarious human cancer types. The information which is obtained from theprocess of performing such cytotoxicity screening may then be used forthe selection of potential therapeutic agents and/or appropriate in vivomodels for efficacy study.

Optionally, the method provided herein may be used for drug discoveryand/or drug analysis. Thus, it may, e.g., be used as a screening methodto screen potential therapeutic agents; or to screen known therapeuticsto analyse their effects. For example, the method may be used to analysethe efficacy of a substance; the mechanism of action of a substance;and/or the safety of a substance. The efficacy may optionally be thetherapeutic efficacy. The safety may optionally be the pharmacologicalsafety.

Optionally the screening may be high-throughput screening. Optionally,the screening may be for, or of, a therapeutic agent effective againstany of the diseases listed elsewhere herein, e.g., cancer or one or morespecific types of cancer.

Thus, the method may optionally comprise exposing a cell population to afirst substance and using the method to analyse the effect of saidsubstance on the cell population. Details of suitable substances arediscussed elsewhere herein.

Optionally, a second substance may be used, e.g., for comparison orcontrol purposes. For example, the method may comprise exposing a firstcell population to a first substance and a second cell population may beexposed to a second substance, analysing the first and the second cellpopulation via mass spectrometry as discussed elsewhere herein andanalysing any differences between the two cell populations. Optionally,the second substance may be a control substance, which may, e.g., be anegative control such as water or a buffer, or a positive control, suchas an agent with a known effect, e.g., a known cytotoxic effect.Optionally, the first and the second cell population may be identicalprior to performance of the method. For example, two samples may betaken from a single cell population to generate 2 cell populations.Optionally, the first and the second cell population may be isogenic.Optionally, the first and the second cell population may bephenotypically and/or genotypically different, e.g., they may bedifferent cell types.

Analysing the effect of said substance on the cell population maycomprise analysing a change in one or more properties of the cellpopulation, details of which are discussed elsewhere herein.

Thus, optionally the method may comprise analysing said spectrometricdata in order to determine whether or not said cell population hasinteracted with said substance in a manner which is of potentialinterest.

An “interaction in a manner which is of potential interest” is meantthat the interaction results in a phenotypic and/or genotypic change.Optionally, the phenotypic and/or genotypic change may be a change inone or more properties of the cell population, details of which areprovided elsewhere herein.

Optionally, the EC₅₀ of a test substance may be tested. EC₅₀ is theconcentration of a drug that gives half-maximal response.

For example, the cytotoxicity of a test substance may be tested, e.g.,the percentage of surviving cells as a function of the concentration ofthe test substance may be measured and cell populations which aresensitive and/or resistant to a test substance may be identified.

Optionally, the method may comprise analysing the susceptibility of acell population to a substance, e.g., the susceptibility of a diseasedcell population to a known or potential therapeutic agent. For example,the susceptibility of a cell population of a particular cancer type toan anti-cancer drug may be analysed.

Optionally, the method may also comprise a step of analysing the effectof an environmental condition of the cell population on the response ofa cell population to said substance. Details of environmental conditionsof the cell population are provided elsewhere herein. Thus, said methodmay optionally comprise the steps of (i) exposing the cell population toa substance; and (ii) changing an environmental condition of the cellpopulation.

Optionally, the steps of (i) exposing the cell population to asubstance; and (ii) changing an environmental condition of the cellpopulation may be carried out simultaneously or sequentially in anyorder. Any of these steps, alone and/or in combination, may optionallybe repeated, e.g., at least 2, 3, 4, 5, 6, 7, 8, 9 or 10 times.

Thus, a cell population may optionally, e.g., be exposed to a substance,and an environmental condition of the cell population may subsequentlybe changed; an environmental condition of the cell population may bechanged, and a cell population may subsequently be exposed to asubstance; and/or a cell population may be exposed to a substance, andan environmental condition of the cell population may simultaneously bechanged.

It will be understood that optionally, one or more different substancesand/or one or more different environmental conditions may be used in anyof these methods. For example, a panel of different substances mayoptionally be used. The panel may, e.g., comprise or consist of membersof a single class of drugs and/or members of two or more classes ofdrugs, e.g., known and unknown drugs.

For example, optionally, the substance may be a cytotoxic and/orcytostatic drug. Thus, e.g., one or more cell populations may be testedwith one or more known cytotoxic/cytostatic drug, e.g., anon-chemotherapy approved cytotoxic/cytostatic drug. Optionally, one ormore cell populations may, e.g., be tested using one or more potentiallynew therapeutic agents or cytotoxic/cytostatic drugs.

Optionally, one or more cell populations may be genetically modified andthe modified cell population may be tested, e.g., with a knowncytotoxic/cytostatic drug, and/or against a panel of potentially newtherapeutic agents or cytotoxic/cytostatic drugs.

Optionally, one or more cell populations may be tested against a firstsubstance and subsequently be tested against a second substance andoptionally one or more further substances.

Analysis of a Change

The optional analysis of a change may be carried out in one or moredifferent ways.

Optionally, a cell population may be analysed via the method providedherein at a first time and at a subsequent further time, e.g., secondtime, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, etc.time.

Thus, optionally, the method may comprise generating said aerosol, smokeor vapour from said target at a first time so as to obtain said firstspectrometric data;

generating aerosol, smoke or vapour from said target, at a subsequenttime;

mass analysing and/or ion mobility analysing the aerosol, smoke orvapour generated at the subsequent time, or ions derived therefrom, soas to obtain second spectrometric data; and

comparing the first and subsequent spectrometric data to determinechanges in the target. The subsequent time may be a second time, third,fourth, fifth, sixth, seventh, eighth, ninth, tenth, etc. time.

Optionally, between the first and a subsequent time, the cell populationmay be manipulated and/or exposed to a substance, which may optionallybe selected from any of the agents listed herein, such as, a test agent.

Optionally, 2 or more, e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15,20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 300, 400, 500, 600, 700,800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, or 10000identical and/or non-identical cell populations may be analysedsimultaneously and/or sequentially. If a group of 3 or more cellpopulations are analysed, then the group may optionally comprise, e.g.,2 or more cell populations that are identical to one another, as well as2 or more cell populations that are non-identical to one another.

Optionally, one or more further test agents and/or reference or controlagents may be used. For example, a first cell population may be exposedto a first test agent and a second cell population may be exposed to afurther test agent, a reference agent or a control agent.

Environmental Conditions

The method allows the analysis of a cell population under a definedenvironmental condition. This may optionally allow, e.g., cellpopulations to be analysed under conditions that mimic in vivoconditions, e.g., the conditions of a tumour or tumour microenvironment.Solid tumours typically develop hostile microenvironments characterizedby irregular vascularization, poor oxygen (O₂) supply, and/or poornutrient supply.

By “defined environmental condition” is meant that at least oneenvironmental factor is controlled. For example, a controlledtemperature or temperature range, or a controlled level of a particularnutrient, may be referred to as a defined environmental condition.

Optionally, the method may involve the analysis of the effect of one ormore defined environmental conditions on a cell population. Optionally,the analysis may be of the effect of a change in one or moreenvironmental conditions on a cell population.

The environmental condition may optionally be a condition that caninfluence cell population growth; differentiation; migration; cellstate; and/or phenotype and/or genotype. Thus, the environmentalcondition may, e.g., be the nature and/or concentration of culture mediacomponents, particularly nutrient and/or mineral concentrations; thenature and extent of cell-cell contacts; temperature; pH; fluid balance;pressure; flow volume; and/or oxygen pressure. For example, the cellpopulation may be exposed to hypoxia.

The environmental condition may optionally be altered by introducing thecell population into a host organism. Thus, optionally, the cellpopulation may be introduced into a host organism, which may optionallybe selected from a human or non-human animal. Optionally, it may be alivestock, domestic or laboratory animal, e.g., be a rodent. Optionally,it may be murine, guinea pig, hamster, rat, goat, pig, cat, dog, sheep,rabbit, cow, horse, alpaca, ferret, fowl, buffalo, and/or monkey. Thus,optionally the cell population may be exposed to, e.g., maintainedand/or grown in, the in vivo environment of a host organism. The effectof such an exposure may optionally be analysed by the method providedherein. Prior to and/or after analysis of a target cell population, oneor more of these conditions may optionally be appropriately modified.Such modification is within the competencies of one of ordinary skill inthe art.

Thus, optionally the method may comprise one or more of the following:changing or varying the concentration of a nutrient which is supplied toa cell population; changing or varying the concentration of a mineralwhich is supplied to a cell population; changing or varying a pH levelat which said cell population is maintained; changing or varying atemperature at which said cell population is maintained; changing orvarying an oxygen, carbon dioxide or other gas level to which said cellpopulation is exposed; changing or varying the concentration of acontamination control substance or an antibiotic to which said cellpopulation is exposed; changing or varying the concentration of acatalyst, inducer or agent which prompts said cell population togenerate a therapeutic or other product; and/or changing or varying alight level to which said cell population is exposed. Optionally, theeffect of any of these changes may be analysed.

For example, the environmental condition may be a culture medium whichhas a low concentration of one or more lipids, and/or a low overalllipid concentration, as discussed below.

Analysis of Lipid Requirements

Compared with non-cancerous cells, cancer cells exhibit significantmetabolic alterations with respect to several critical nutrients andsubstrates, e.g., the metabolism of glucose and glutamine. Cancer cellsalso exhibit increased demand for fatty acids, which they may synthesizeendogenously from carbohydrates, such as citrate, or take up fromexogenous sources. Lipoprotein lipase (LPL) and/or the fatty acidchannel protein CD36 may be involved in the uptake of fatty acids,whereas fatty acid synthase (FASN) may be involved in fatty acidsynthesis. The elevated rates of lipid synthesis occur through increasedexpression of various lipogenic enzymes. Increased lipid productionappears to be critical for cancer cell survival, and expression of keylipogenic enzymes may be strongly correlated with cancer progression.Fatty acids may be incorporated into membranes as phospholipids, storedin lipid droplets, and/or used for the production of signalling lipids.

The de novo synthesis of lipids, which may be called lipogenesis,involves the conversion of acetyl-CoA into fatty acids, which may thenbe assembled into lipids. Acetyl-CoA may be derived from a variety ofsources, e.g., from carbohydrates, e.g., via the glycolytic pathway.

Increased lipogenesis is a characteristic of a wide variety of cancers,so lipogenesis pathways are a potential anti-cancer target. To analysethe therapeutic potential of lipid synthesis inhibitors, a betterunderstanding of the relationship between de novo lipid synthesis andexogenous lipids and their respective role in cancer cell proliferationand therapeutic response to lipogenesis inhibitors is of criticalimportance.

Thus, cancer cells require fatty acids. They may synthesise fatty acidsfrom carbohydrates, and/or take up fatty acids from their surroundings,i.e. exogenous fatty acids. Some types of cancer cells appear to dependon exogenous fatty acids for survival, whereas other cancer types arecapable of surviving in the absence of exogenous fatty acids.

Optionally, the method may therefore be used to analyse whether a cellpopulation has a requirement for exogenous lipids, e.g., fatty acids,and/or the extent to which a cell population requires exogenous lipids,e.g., fatty acids. Thus, the method may, e.g., allow a determination tobe made as to whether a particular cell population has a requirement forexogenous fatty acids. The method may, e.g., allow a determination to bemade that a particular cell population is capable of a high level oflipogenesis. Such a population may be referred to as having a“lipogenic” or “highly lipogenic” phenotype and/or genotype.

Thus, the method may optionally involve culturing a cell population in aculture medium which has a low concentration of one or more lipids,and/or a low overall lipid concentration. Optionally, the method mayinvolve culturing a cell population in a host organism. Such anenvironmental condition may simulate in vivo conditions of tumour cells,particularly cells in the centre of a tumour. The effect of such anenvironmental condition may be analysed by the method provided herein,and/or the effect of a substance on a cell population cultured underthis environmental condition may be analysed.

If a cell population is not provided with sufficient levels of lipidsvia its culture medium, then one or more of the following may occur: thegrowth of the cell population may slow and/or halt; a proportion and/orall of the cell population may die; and/or the cells may synthesizelipids.

Cancer cells with a lipogenic phenotype and/or genotype may differ intheir susceptibility to a substance. As mentioned above, the methodprovided herein may be used to determine whether a cell population has alipogenic phenotype and/or genotype, and or whether it is susceptible toa substance, such as an anti-cancer agent, e.g., an inhibitor of alipogenic pathway.

According to various embodiments REIMS analysis may be extended tocancer phenotyping studies.

Isotope Studies

Optionally, the method may be used in or with isotope studies.

Isotopes are variants of a particular chemical element which differ inneutron number, whilst having the same number of protons in each atom.Isotope studies may, e.g., involve the use of stable isotopes, i.e.non-radioactive isotopes. For example, isotopes of hydrogen (H), carbon(C), nitrogen (N), oxygen (O), fluorine (F) and/or sulphur (S) may beused. The term “different types of isotopes” is used to mean isotopes ofdifferent elements, so an isotope of C is a different type of isotopefrom an isotope of N.

In nature, one isotope of each element is typically most abundant, andany other stable isotopes of the element that may exist are typicallyfar less abundant. For example, 1H is far more abundant than ₂H, ₁₂C isfar more abundant than ₁₃C, ₁₄N is far more abundant than ₁₅N, ₁₆O isfar more abundant than ₁₇O or ₁₈O, and ₃₂S is far more abundant than₃₄S. The less abundant isotopes are the heavier ones, so they may bereferred to as a “heavy isotope”. In isotope studies, cells may beexposed to one or more heavy isotopes and cellular processes may then beanalysed by analysing the fate of the heavy isotope(s).

Different nonradioactive stable isotopes can be distinguished by massspectrometry, so the method provided herein may optionally be used in orwith isotope studies.

Thus, optionally, the cell population may be exposed to one or moreheavy isotopes, e.g., to one or more substances comprising or consistingof one or more heavy isotopes. The substance may optionally be selectedfrom any of the substances listed elsewhere herein, e.g., any nutrients,e.g., glucose, glutamine o the like. A substance comprising orconsisting of one or more heavy isotopes may be referred to as a“heavy-isotope substance”. A heavy-isotope substance may optionallycomprise a single heavy-isotope, 2 or more heavy-isotopes, or consist ofheavy-isotopes. A heavy-isotope substance may optionally comprise asingle type of heavy isotope or 2 or more, e.g., at least 2, 3, 4, 5, or6 different types of heavy isotopes.

A substance may optionally be isotopically defined, i.e. it may bepossible to use a substance in which one or more specific atoms arereplaced with one or more heavy isotopes, which may allow an analysis ofthe fate of specific parts of a substance. Optionally, an analysis witha substance having a first atom replaced with a heavy isotope may becompared to an analysis with the corresponding substance having adifferent atom replaced with a heavy isotope.

For example, a heavy-isotope substance, such as, a nutrient, e.g.,carbon source, may be used and the method may optionally be used toanalyse whether and/or how the nutrient is used by the cell population.Thus, optionally, lipid metabolism, e.g., anabolism and/or catabolismmay be analysed. In particular, the method may optionally be used toanalyse the depletion or enrichment of a heavy isotope type in one ormore metabolites, such as, fatty acid type(s). Thus, e.g., the presenceor absence, and/or relative abundance, of one or more metabolites, fattyacids, lipids and/or biomarkers may be analysed prior to and/or afterexposure of a cell population to a heavy isotope. Optionally, theanalysis may be carried out at 2 or more time points, e.g., to monitor achange over time in the presence or absence, and/or relative abundance,of one or more metabolites, fatty acids, lipids and/or biomarkers.

Optionally, a cell population may be exposed to at least 2 types ofheavy isotopes and/or at least 2 types of heavy isotope substancessimultaneously and/or sequentially. Optionally, a cell population may beexposed to a first type of heavy isotope at a first time point and to asecond type of heavy isotope at a second time point. Optionally, a cellpopulation may be exposed to a first type of heavy isotope substance ata first time point and to a second type of heavy isotope substance at asecond time point. For example, a cell population may be exposed to aheavy-isotope glucose at a first time point and to a heavy-isotopeglutamine at a second time point.

Analysis of Radio-Tracers

Positron Emission Tomography (PET) is a radiotracer imaging technique,in which tracer compounds labelled with positron-emitting radionuclidesare injected into the subject of the study. These radio-tracer compoundscan then be used to track biochemical and physiological processes invivo. One of the prime reasons for the importance of PET in medicalresearch and practice is the existence of positron-emitting isotopes ofelements such as carbon, nitrogen, oxygen and fluorine which may beprocessed to create a range of radio-tracer compounds which are similarto naturally occurring substances in the body.

Optionally, the radio-tracer may be a compound labelled with ¹¹C, ¹³N,¹⁵O, and/or ¹⁸F. Optionally, it may be selected from the compoundslisted in the table below.

Tracer Physiological Isotope compound process or function Typicalapplication ¹¹C methionine protein synthesis oncology ¹¹C flumazenilbenzodiazepine epilepsy receptor antagonist ¹¹C raclopride D2 receptoragonist movement disorders ¹³N ammonia blood perfusion myocardialperfusion ¹⁵O carbon dioxide blood perfusion brain activation studies¹⁵O water blood perfusion brain activation studies ¹⁸F Fluoro-deoxy-glucose metabolism oncology, neurology, glucose cardiology ¹⁸F Fluorideion bone metabolism oncology ¹⁸F Fluoro- hypoxia oncology - response tomizonidazole radiotherapyThus, e.g., if the biologically active molecule chosen isfluorodeoxyglucose (FDG), an analogue of glucose, the concentrations oftracer will indicate tissue metabolic activity as it corresponds to theregional glucose uptake. Use of this tracer to explore the possibilityof cancer metastasis (i.e., spreading to other sites) is the most commontype of PET scan in standard medical care (90% of current scans).

Optionally, a cell population may be exposed to a radio-tracer and themethod may be used to analyse the location and/or concentration of aradio-tracer. Thus, the method may optionally be used to analyse themetabolism of a compound labelled with a positron-emitting radionuclide.

Isogenic Cell Populations

Optionally, the method may involve the use of 2 or more cell populationsthat are isogenic except for one or more genetic regions of interest.The term “isogenic” is used in the art to indicate that 2 cellpopulations are genetically identical or share essentially the samegenetic information, except for one or more genetic regions of interest.Typically, 2 isogenic cell populations will differ in a single gene,which may optionally be linked to a reporter gene in which the isogeniccell populations may also differ.

Optionally, isogenic cell populations may differ with respect to anendogenous gene, e.g., one cell population may have a wild-typeendogenous gene and another cell population may have a mutant version ofsaid gene. Optionally, the mutant version may have an alteredfunctionality or be a knock-out.

Optionally, isogenic cell populations may differ with respect to anexogenous gene, e.g., one cell population may comprise a first versionof an exogenous gene and another cell population may have a secondversion of said exogenous gene; or one cell population may comprise afirst exogenous gene and another cell population may have a secondexogenous gene.

The method may thus optionally be used to analyse differences between 2or more isogenic cell populations. The use of isogenic cell populationsmay be useful, e.g., to analyse the effect of a modification or change,e.g., to analyse the effect of a substance on a cell population; toanalyse the effect of an environmental change on a cell population;and/or to analyse the production of a substance by a cell population.

Isogenic cell populations may be obtained, e.g., by transfecting a firstcell population with a first vector that encodes a first transgene and afirst marker and transfecting a second cell population with a secondvector that encodes a second transgene and a second marker, the secondbeing different from the first.

Optionally, the marker may, e.g., be a fluorescent marker, details ofwhich are provided elsewhere herein.

Culture and analysis via the method provided herein of both cellpopulation allows, e.g., screening for compounds with selectiveactivity, e.g., toxicity, towards a gene of interest. Such drugscreening is broadly applicable for mining therapeutic agents targetedto specific genetic alterations responsible for disease development.

Substances Such as Test Agents and/or Cytotoxic Anticancer Drugs

As mentioned elsewhere herein, the method may optionally be used toanalyse the effect of a substance on a cell population; and/or toanalyse the production of a substance.

Thus, the method may optionally involve the direct or indirect analysisof one or more substances. Unless otherwise stated, the terms“substance”, “compound”, “molecule” and “biomolecule” are usedinterchangeably herein.

As mentioned elsewhere herein, the method may also optionally involve astep of administering a treatment to a subject for which the target cellpopulation is a model. Such a treatment step may, e.g., involve theadministration of a therapeutic agent, which may optionally comprise orconsist of any of the substances mentioned herein.

The compound may optionally be intracellular and/or extracellular. Itmay optionally be endogenous, i.e. produced by the cell population,and/or exogenous, i.e. added to the cell population.

The compound may optionally comprise or consist of any of the compoundsor classes of compounds mentioned herein, e.g., any of the biomarkercompounds mentioned herein.

Optionally, it may comprise or consist of, for example, a lipid, suchas, a glycolipid or phospholipid; carbohydrate; DNA; RNA; protein, e.g.,an antibody, enzyme or hormone; polypeptide, such as, a ribosomalpeptide or a non-ribosomal peptide; oligopeptide; lipoprotein;lipopeptide; amino acid; and/or chemical molecule, optionally an organicchemical molecule.

The compound may optionally be linear, cyclic or branched.

The compound may optionally be a metabolite, such as, a primary or asecondary metabolite; an antibiotic; a quorum sensing molecule; a fattyacid synthase product; a pheromone; a protein; a peptide; and/or abiopolymer. It may optionally be an antibody or hormone.

The compound may optionally be characterised by one or more of thefollowing functional groups: alcohol, ester, alkane, alkene, alkyne,ether, ketone, aldehyde, anhydride, amine, amide, nitrile, aromatic,carboxylic acid, alkyl halide, and/or carbonyl. Optionally, it mayadditionally be identified as being primary, secondary or tertiary,e.g., a primary alcohol, a secondary amine, or the like.

The substance may optionally be a test agent or a drug.

The substance may optionally be a known drug, e.g., an anti-cancer drug,e.g., a cytostatic and/or cytotoxic agent, which may optionally beselected from any of the substances listed below.

The substance may optionally be, e.g., an aromatase inhibitor; ananti-angiogenic agent; a Tubulin-binding agent; an inhibitor oflipogenic pathways; and/or a cytostatic agent; optionally selected froman alkylating agent, a cross-linking agent, an intercalating agent, anucleotide analogue, an inhibitor of spindle formation, and/or aninhibitor of topoisomerase I and/or II.

It may, for example, be an antibody specific for a receptor expressed bycancer cells, which may optionally be conjugated to a chemotherapy drugor to a radioactive particle.

The antibody may optionally, for example, be selected from a HER-2/neuspecific monoclonal antibody, such as, Trastuzumab (Herceptin);Adecatumumab, alemtuzumab, Blinatumomab, Bevacizumab, Catumaxomab,Cixutumumab, Gemtuzumab, Rituximab, Trastuzumab, and/or Ibritumomab.

The substance may optionally be, e.g., an anthracycline, anEpipodophyllotoxin, a Dactinomycin, a Camptothecin, a Taxane, a Vincaalkaloid, Soraphen A, and/or Simvastatin Cytotoxic anticancer drugs(sometimes known as antineoplastics) describe a group of medicines thatcontain chemicals which are toxic to cells. The cytotoxic drugs preventcell replication and growth and hence are useful in the treatment ofcancer. Most of the commonly used cytotoxic anticancer drugs werediscovered through random high-throughput screening of syntheticcompounds and natural products in cell-based cytotoxicity assays. Mostof the compounds are DNA-damaging agents with a low therapeutic index.

An initial National Cancer Institute (NCI) high-throughput screen usedthe highly chemosensitive P388 leukemia cell line. However, this screenfailed to identify drugs that were active against common adult solidtumours. As a result, the NCI implemented a new in vitro screenconsisting of 60 human tumour cell lines representing nine common formsof cancer.

The following table shows commonly used natural product anticancer drugsand their corresponding mechanism of action:

Drug class Mechanism of action Anthracyclines Topoisomerase IIinhibitors Epipodophyllotoxins Topoisomerase II inhibitors DactinomycinTopoisomerase II inhibitors Campthothecins Topoisomerase I inhibitorsTaxanes Tubulin-binding agents Vinca alkaloids Tubulin-binding agents

The substance may optionally be selected from, e.g., anastrozole;azathioprine; bcg; bicalutamide; chloramphenicol; ciclosporin;cidofovir; coal tar containing products; colchicine; danazol;diethylstilbestrol; dinoprostone; dithranol containing products;dutasteride; estradiol; exemestane; finasteride; flutamide; ganciclovir;gonadotrophin, chorionic; goserelin; interferon containing products(including peginterferon); leflunomide; letrozole; leuprorelin acetate;medroxyprogesterone; megestrol; menotropins; mifepristone; mycophenolatemofetil; nafarelin; oestrogen containing products; oxytocin (includingsyntocinon and syntometrine); podophyllyn; progesterone containingproducts; raloxifene; ribavarin; sirolimus; streptozocin; tacrolimus;tamoxifen; testosterone; thalidomide; toremifene; trifluridine;triptorelin; valganciclovir; and/or zidovudine. These substances mayoptionally be referred to as non-chemotherapy approvedcytotoxic/cytostatic drugs.

Alternatively or in addition, the substance may optionally be selectedfrom, e.g., aldesleukin; alemtuzumab; amsacrine; arsenic trioxide;asparaginase; bleomycin; bortezomib; busulphan; capecitabine;carboplatin; carmustine; cetuximab; chlorambucil; cisplatin; cladribine;cyclophosphamide; cytarabine; dacarbazine; dactinomycin; daunorubicin;dasatinib; docetaxel; doxorubicin; epirubicin; estramustine; etoposide;fludarabine; fluorouracil; gemcitabine; gemtuzumab; hydroxycarbamide;idarubicin; ifosfamide; imatinib mesylate; irinotecan; lomustine;melphalan; mercaptopurine; methotrexate; mitomycin; mitotane;mitoxantrone; oxaliplatin; paclitaxel; pentamidine; pentostatin;procarbazine; raltitrexed; rituximab; temozolomide; thiotepa; topotecan;trastuzumab; vidaradine; vinblastine; and/or vincristine. Thesesubstances may optionally be referred to as non-chemotheraphy approvedcytotoxic/cytostatic drugs.

The substance may optionally be selected, e.g., from Mescaline, PCP(Phencyclidine), Psilocybin, LSD, Heroin, Morphine, Codeine,dextroamphetamine, bupropion, cathinone, lisdexamfetamine, Allobarbital,Alphenal (5-allyl-5-phenylbarbituric acid), Amobarbital, Aprobarbital,Brallobarbital, Butobarbital, Butalbital, Cyclobarbital,Methylphenobarbital, Mephobarbital, Methohexital, Pentobarbital,Phenobarbital, Secobarbital, Talbutal, Thiamylal, and/or Thiopental.Ranitidine, phenylalanine PKU, dimethylamylamine, cocaine, diazepam,androstadienedione, stigmastadienone, androsteronehemisuccinate,5α-androstan-3β,17β-diol-16-one, androsterone glucuronide,epitestosterone, 6-dehydrocholestenone, phenylalanine, leucine, valine,tyrosine, methionine, sitamaquine, terfenadine, prazosin, methadone,amitripyline, nortriptyline, pethidine, DOPA, ephedrine, ibuprofen,propranolol, atenolol, acetaminophen, bezethonium, citalopram,dextrorphan, paclitaxel, proguanil, simvastatin, sunitinib, telmisartan,verapamil, amitriptyline, pazopanib, tamoxifen, imatinib,cyclophosphamide, irinotecan, docetaxel, topotecan, acylcarnitines(C2-C18), nicotine, cotinine, trans-3-hydroxycotinine, anabasine,amphetamine, amphetamine-like stimulants, methamphetamine, MDA, MDMA,MDEA, morphine, Δ⁹-THC, tacrolimus, benzethonium, meprobamate,O-desmethyl-cis-tramadol, carisoprodol, tramadol, nordiazepam, EDDP,norhydrocodone, hydromorphone, codeine, temazepam, noroxycodone,alprazolam, oxycodone, buprenorphine, norbuprenorphine, fentanyl,propoxyphene, 6-monoacetylmorphine, caffeine, carbadox, carbamazepine,digoxigenin, diltiazem, diphenhydramine, propanolol, sulfadiazine,sulfamethazine, sulfathiazole, thiabendazole, ketamine, norketamine,BZE, AMP, MAMP, and/or 6-MAM.

Optionally, one or more cell populations may be tested with a knownnon-chemotherapy approved cytotoxic/cytostatic drug. Optionally, one ormore cell populations may be tested using a panel of potentially newtherapeutic agents or cytotoxic/cytostatic drugs.

Optionally, one or more cell populations may be genetically modified andthe modified cell populations may be tested with a knowncytotoxic/cytostatic drug or against a panel of potentially newtherapeutic agents or cytotoxic/cytostatic drugs.

Optionally, one or more cell populations may be tested with a knowncancer chemotherapy approved cytotoxic/cytostatic drug. Optionally, oneor more cell populations may be tested using a panel of potentially newtherapeutic agents or cytotoxic/cytostatic drugs.

Optionally, one or more cell populations may be genetically modified andthe modified cell population may be tested with a known cancerchemotherapy approved cytotoxic/cytostatic drug or against a panel ofpotentially new therapeutic agents or cytotoxic/cytostatic drugs.

The substance may, e.g., be an antimicrobial. The term “antimicrobial”includes any agents that act against any type of microbe. Thus, theantimicrobial may optionally be selected from antibacterial, anantiviral, an antifungal, and an antiprotozoal. More particularly, itmay optionally be selected from aminoglycosides, beta-lactamantibiotics, chloramphenicol, fluoroquinolones, glycopeptides,lincosamides, macrolides, polymixins, rifampins, streptogramins,sulphonamides, tetracyclines, and/or diaminopyrimidines.

The Aminoglycoside may optionally be selected from gentamicin,tobramycin, amikacin, streptomycin, kanamycin. The beta-lactamantibiotic may optionally be selected from a penicillin such asmethicillin, penicillin, amoxicillin, ampicillin, carbenicillin,oxacillin or nafcillin; a cephalosporin, such as, cephalothin,cefamandole, cefotaxime, ceftazidime, cefoperazone, or ceftriaxone; acarbapenem, such as, imipenem, meropenem, ertapenem, ordoripenem; or amonobactam, such as, aztreonam. The fluoroquinolone may optionally beselected from Enrofloxacin, ciprofloxacin, Danofloxacin, Difloxacin,Ibafloxacin, Marbofloxacin, Pradofloxacin and Orbifloxacin. Theglycopeptide may optionally be selected from vancomycin, teicoplanin andavoparcin. The lincosamide may optionally be selected from Lincomycin,Clindamycin and Pirlimycin. The macrolide may optionally be selectedfrom Erythromycin, Tylosin, Spiramycin, Tilmicosin and Tulathromycin.The polymixin may optionally be selected from Polymixin B and colistin(Polymixin E). The rifampin may optionally be selected from Rifampin,Rifabutin and Rifapentine. The Streptogramin may optionally be selectedfrom Virginiamycin. The sulfonamide may optionally be selected fromSulfadiazine, sulfamethoxazole and sulfadoxine. The tetracycline mayoptionally be selected from Chlortetracycline, oxytetracycline,demethylchlortetracycline, rolitetracycline, limecycline, clomocycline,methacycline, doxycycline and minocycline. The Diaminopyrimidine mayoptionally be selected from Trimethoprim, Aditoprim, Baquiloprim and/orOrmetoprim.

The substance may, e.g., be an anti-viral drug.

The substance may, e.g., be an anti-inflammatory drug, optionallyselected from, e.g., steroids, diclofenac, ibuprofen, naproxen,celecoxib, mefenamic acid, etoricoxib, indomethacin, and/or aspirin.

Target Based Drug Discovery Identification of valid molecular targetshas led to target-based drug discovery at the protein level asillustrated by the development of Imatinib (INN). Imatinib is atyrosine-kinase inhibitor which is used in the treatment of multiplecancers including Philadelphia chromosome-positive (Ph⁺) chronicmyelogenous leukemia (CML).

Gleevec is the beta crystalline form of imatinib mesilate, the mesylatesalt of imatinib.

In order to survive, cells need signalling through proteins (signalcascade) to keep them alive. Some of the proteins in this cascade use aphosphate group as an “on” switch. This phosphate group is added by atyrosine kinase enzyme. In healthy cells, these tyrosine kinase enzymesare turned on and off as needed. In Ph-positive CML cells, one tyrosinekinase enzyme, BCR-AbI, is stuck on the “on” position, and keeps addingphosphate groups. Imatinib blocks this BCR-AbI enzyme and stops it fromadding phosphate groups. As a result, these cells stop growing and thecells die by a process of cell death (apoptosis). Because the BCR-AbItyrosine kinase enzyme exists only in cancer cells and not in healthycells, imatinib works as a form of targeted therapy—only cancer cellsare killed through the drug's action. In this regard, imatinib was oneof the first cancer therapies to show the potential for such targetedaction and is often cited as a paradigm for research in cancertherapeutics.

Imatinib was discovered by screening compound libraries for inhibitorsof the protein kinase activity in vitro. Many of the proteins involvedin cell cycle regulation, signal transduction and the regulation ofapoptosis are enzyme or receptors. As a result, they are potentiallyamenable to inhibition by small molecules.

Nutrients

Cell populations require nutrients for survival and/or growth. One ormore suitable nutrients may therefore be used to culture a cellpopulation. Optionally, a mixture of different nutrients may be used,e.g., a mixture comprising one or more of the nutrients listed below. Asdiscussed elsewhere herein, the type and/or level of any nutrients maybe altered, e.g., when analysing the effect of environmental conditions.

Any nutrient may optionally be a heavy-isotope nutrient.

Suitable nutrients are well known, but a nutrient may optionallycomprise or consist of, e.g., a carbohydrate, optionally selected frommonosaccharides, disaccharides, oligosaccharides, and/orpolysaccharides. It may optionally be selected from sucrose, glucose,fructose, maltose, starch, lactose, galactose, lactulose, and/ortrehalose.

A nutrient may optionally comprise or consist of, e.g., an amino acid, apeptide, a polypeptide, or protein, optionally selected from anessential amino acid, a non-essential amino acid, and/or a peptide,polypeptide or protein comprising one or more essential and/ornon-essential amino acids. Essential amino acids may be selected fromphenylalanine, valine, threonine, tryptophan, methionine, leucine,isoleucine, lysine, and/or histidine. Optionally, a nutrient may beglutamine.

A nutrient may optionally comprise or consist of a vitamin, e.g.,vitamin A, B, C, D, or E.

A nutrient may optionally comprise or consist of a lipid, e.g., a fattyacid, lecithin, and/or a sterol.

Culture Media

The cell population may be maintained in a cell culture medium. The cellculture medium may optionally comprise a complex component, such asblood or a derivative thereof, e.g., serum, or be a serum-free definedmedium. Unless it is desired to test an environmental factor relating tothe cell culture medium, the cell culture medium may optionally besterile, isotonic, have a physiological pH, and/or comprise all of theminerals and nutrients required by the cell population.

One or more of the following culture medium components may optionally beoptimised or altered: (i) Nutrients, which may be optimised to includeall essential nutrients at sufficient levels, or which may be altered,e.g., to insufficient levels of one or more nutrients; minerals whichmay be optimised to include all essential minerals at sufficient levels,or which may be altered, e.g., to insufficient levels of one or moreminerals;

(ii) pH, which may optimised to be physiological, e.g., between 6.5 and7.5, e.g., about 6.8, 7, 7.2 or 7.4, or which may be altered, e.g., tobe above or below physiological, e.g., below 6.5 or about 7.5;

(iii) temperature, which may be optimised to be at a physiologicaltemperature of about 37° C., or may be altered to be abovephysiological, e.g., at least 37.5, 38, 38.5, 39, 39.5, 40, 40.5, 41,41.5, 42, 42.5, 43, 44, or 45, and/or up to 70, 60, 55, 50, 45, 40° C.,or below physiological, e.g., about 36, 35, 34, 33, 32, 31 or 30° C.;

(iv) levels of gases, e.g., oxygen and/or CO₂, to which the cellpopulation is exposed to, which may be optimised to be at aphysiological levels, or may be altered to be above physiological orbelow physiological, e.g., hypoxic conditions, such as <2% oxygen. Forexample, oxygen levels may be set at about 20% or at about 2-8%, and/orCO₂ levels may be set at about 5%.

Optionally, the cell population may be cultured on or with feeder cells,e.g., on a layer of feeder cells, or the cell population may be culturedin the absence of any feeder cells.

Optionally, the cell population may be cultured in the presence of oneor more hormones, cytokines, chemokines and the like.

Oxidative Stress

The method may optionally be used to analyse oxidative stress. Forexample, the cell population may be exposed to a substance and/orenvironmental condition that causes or induces oxidative stress.Optionally, a biomarker of oxidative stress may be analysed. Thebiomarker may, e.g., be selected from any of the substances mentionedbelow.

Oxidative stress, which can be defined as an imbalance between theproduction and removal of reactive oxygen species (ROS) has beenimplicated in many types of nerve cell death in the central nervoussystem (CNS) and also in the eye.

The presence of high concentrations of ROS can overwhelm the cell'snatural defence mechanisms and activate pathways that lead to programmedcell death. Although the precise mechanisms that give rise to theincreases in ROS may vary from condition to condition, the cell deathpathways appear to have several features in common. Among these featuresis that death proceeds by a series of steps and that the inhibition ofany one of these steps can often rescue the nerve cells from death.Certain agents can ameliorate the cell death process in circumstancesthat involve oxidative stress.

One model of the induction of oxidative stress in nerve cells utilisesthe amino acid glutamate. Glutamate is the major excitatoryneurotransmitter in the CNS and in the eye, but it has also beenimplicated in nerve cell death after acute neurologic insults and inseveral different ocular diseases, including glaucoma, diabeticretinopathy, and various forms of retinal ischemia.

Although glutamate is present in synaptic nerve terminals in millimolarconcentrations, the extracellular concentrations are normally high onlyduring the brief periods of synaptic transmission. However, certainforms of injury can result in extended periods of elevated extracellularglutamate levels. High levels of extracellular glutamate have been shownto be toxic to nerve cells in culture through two distinct processes:excitotoxicity which occurs through the activation of ionotropicglutamate receptors, and a programmed cell death pathway calledoxidative glutamate toxicity, or oxytosis which is mediated by a seriesof disturbances to the intracellular redox system.

Increases in the endogenous levels of ROS are key elements in the celldeath cascade in both of these processes.

In glutamate excitotoxicity, the activation of ionotropic glutamatereceptors leads to an excessive influx of Ca⁺², which activates a celldeath cascade involving the accumulation of mitochondrially generatedROS.

In oxidative glutamate toxicity, glutamate inhibits the uptake ofcystine, which is essential for glutathione (GSH) biosynthesis,resulting in the depletion of GSH from the cells. GSH is the mostabundant intracellular thiol and the major intracellular antioxidant.The glutamate-induced loss of GSH from cells leads to a biphasicincrease in mitochondrially derived ROS that can eventually reach levelsmany times higher than those in untreated cells. These high levels ofROS lead to Ca⁺² influx, which is mediated by a cobalt-sensitive,cGMP-gated Ca⁺² channel and subsequently to cell death.

The central role of ROS in the cell death cascade in oxidative glutamatetoxicity is demonstrated by the protection provided by exogenousantioxidants such as vitamin E and propyl gallate. The importance ofmitochondrial ROS production is further demonstrated by the observationthat the mitochondrial uncoupler cyanidep-trifluoromethoxyphenylhydrazone (FCCP), as well as other mitochondrialinhibitors, blocks cell death in this model, by inhibiting ROSproduction.

Substance Production and/or Utilisation by Cell Populations

Cell populations may be used for the production of substances, such as,biopharmaceuticals, e.g., antibodies, hormones and/or cytokines. Cellpopulations may utilise a first substance as a substrate to produce asecond substance. Cell populations may be used to break down substances,optionally into useful and/or less harmful substances. They may, e.g.,be used to break down industrial waste products, pollutants, herbicides,pesticides, explosives, and the like. The method may thereforeoptionally be used to analyse the ability of a cell population toutilise and/or produce a substance; and/or to analyse the utilisationand/or production of a substance by a cell population.

Optionally, the method may involve the purification of a substance, soit may include a step of purifying a substance. A step of purifying thesubstance may, e.g., comprise one or more of lysis of cells;centrifugation, e.g., to achieve isopycnic banding and/ornon-equilibrium settling; filtration; membrane separation, which may,e.g., be microfiltration, ultrafiltration, and/or dialysis; extraction,which may, e.g., be fluid extraction, and/or liquid/liquid extraction;precipitation, which may, e.g., be fractional precipitation;chromatography, which may, e.g., be ion-exchange chromatography, gelfiltration chromatography, affinity chromatography, hydrophobicinteraction chromatography, high performance liquid chromatography(“HPLC”), and/or adsorption chromatography. Optionally, it may involveprecipitation of a free acid form of said substance, and, optionally,conversion of a free acid form of said substance to a salt of saidcompound.

Identification of Utilisation/Production Cell Populations

Optionally, the method may be used as a screening method, e.g., toidentify a suitable utilisation/production cell population, and/or todistinguish between cell populations with differentutilisation/production properties.

Cells may optionally be manipulated, e.g., genetically manipulated, togenerate a cell population having one or more desired properties.Optionally, the method may involve an analysis to identity/select a cellpopulation which has successfully been manipulated, e.g., to identify acell population having the desired genotype and/or phenotype.

Conventional methods for deriving a suitable utilisation/production cellpopulation, e.g., a high-producing cell line from parental line, may bequite time-consuming and laborious and may, e.g., take more than sixmonths in industrial settings. The first step may be geneticmanipulation, which is exemplified in the discussion below by theinsertion of a transgene. Once the transgene enters the nucleus, theintegration site of the gene may be random, and expression of thetransgene may, in part, be dictated by the surrounding chromosomalstructure. High expression of the transgene may be very desirable.Optionally, the method provided herein may be used to speed up theprocess of identifying/selecting a suitable production cell population.

A large number, e.g., a pool of at least or about 50,000, 40,000,30,000, 20,000, 10,000, 5,000, 1,000 or 500 cell populations may bescreened to select a small number, e.g., about or no more than 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 80, 100 or 200 candidate cellpopulations.

Optionally, one or more strategies may be used to improve the generationand/or selection of cells that have a transgene integrated at atranscriptionally active site. For example, the transgene construct mayoptionally include: a mutant DHFR gene with reduced enzymatic activityor a DHFR gene driven by a weak promoter; chromatin opening elementslike scaffold or matrix attachment regions (S/MARs) and/or ubiquitouschromatin opening elements (UCOS) to promote accessibility of DNA fortranscription at the integration site; and/or one or more antibioticresistance gene(s).

If the DHFR system is used, then, following mutagenesis, cells stablyexpressing the transgene construct and hence the DHFR enzyme mayoptionally be selected using cultivation in the absence of glycine,hypoxanthine and thymidine. Optionally, the cells may be exposed to MTXto increase the selective pressure, which may result in genomicrearrangements and amplification of the transgene construct.

If an antibiotic resistance gene is used, then, following mutagenesis,cells stably expressing the transgene construct and hence the antibioticresistance factor, may optionally be selected using the relevantantibiotic.

A selection strategy, e.g., one of the ones mentioned herein, may yielda heterogeneous population of cells having different transgene constructintegration sites, copy numbers and the like.

Optionally, a series of limiting dilutions, e.g., in multi-well plates,may be carried out to isolate uniform cell populations, which mayoptionally be screened to select candidate cell lines.

Optionally, candidate cell populations may be evaluated in more detailand/or on a larger scale to select one or more final candidates.

Optionally, the method provided herein may be used to analyse cells atany stage of such a process of deriving a suitableutilisation/production cell population. Optionally, expression of thetransgene and/or one or more factors that influence the growth,efficiency and/or productivity of a cell may be analysed. This allowsthe rapid selection of a small number of candidates. For example, thepool of cells may be reduced by a factor of about or at least 10, 20,30, 40, 50, 60, 70, 80, 90, 100, 200, 500 or 1000.

Thus, optionally, the method may involve analysing a plurality of cellcultures, each cell culture comprising one or more cell populations.This may optionally involve generating a plurality of spectrometric dataand determining from said plurality of spectrometric data a first subsetof cell cultures which are of potential interest for utilising asubstance and/or producing a biosynthesized substance.

The method may optionally further involve the use of liquidchromatography based analysis, e.g., liquid chromatography massspectrometry (“LCMS”) analysis; liquid chromatography ion mobilityspectrometry (“LCIMS”) analysis; liquid chromotography tandem massspectrometry (“LCMS/MS”) analysis; liquid chromotography followed byMS^(E) spectrometry (“LCMS^(E)”) analysis; liquid chromotographyfollowed by ion mobility separation and then mass spectrometry(“LC-IMS-MS”) analysis; and/or liquid chromotography followed by ionmobility separation and then MS^(E) spectrometry (“LC-IMS-MS^(E)”)analysis. Such a liquid chromatography based analysis may optionally beused to analyse said first subset of cell cultures, e.g., to generate asecond subset of cell cultures. Optionally, the method may compriseclassifying or dividing cell populations into subsets based onspectrometric data, liquid chromatography based analysis data, or acombination of spectrometric data and liquid chromatography basedanalysis data.

However, the ambient ionisation mass spectrometry methods mentionedherein are must faster than liquid chromatography based analysis, so,optionally, the method optionally does not involve the use of a liquidchromatography based analysis, e.g., any of the ones listed above.

Thus, optionally, the method may be used in the process of drugmanufacture and production.

Conventionally, a very large number (e.g., approximately 50,000) ofpotential batches of a cell culture may be produced. Liquidchromatography mass spectrometry (LCMS) may then be performed on each ofthe cell cultures in order to determine a small subset of cell cultureswhich are of greatest interest in terms of taking on into fullproduction.

However, it will be appreciated that subjecting approximately 50,000separate batches of cell populations to LCMS analysis is a complex andtime consuming process.

One particular advantage of the method provided herein is that themethod provided herein enables experimental results to be produced onessentially an instantaneous basis. Furthermore, the method providedherein lends itself to automation and a large number of cell culturescan be analysed either in sequence and/or in parallel in a comparativelyshort period of time. Certainly, it is possible to analyse approximately50,000 separate batches of cell cultures on a timescale which is severalorders of magnitude faster than conventional LCMS approaches.

Accordingly, one particular application of REIMS and related ionisationtechniques is the ability to analyse a large number of cell cultures ina short period of time.

This analysis enables the large number of cell cultures, e.g., 50,000,to be reduced to a very small candidate list of, for example, just tencell cultures which can then be taken on to full production/utilisation.

Alternatively, other embodiments are contemplated wherein REIMS analysismay be performed on the approximately 50,000 batches enabling a firstsubset of cell cultures to be established. The first subset of cellcultures may comprise, for example, approximately 1000 batches. LCMS canthen be performed on this reduced number of 1000 samples in order todetermine a second subset of cell cultures (e.g., 10) which are the mostpromising to be taken on to full production. Although this alternativeapproach still involves using LCMS, the two-stage process still resultsin considerable time savings since only e.g., 1000 batches need to beanalysed by LCMS (c.f. approximately 50,000 as per the conventionalapproach).

Substance Utilisation/Production and Quality Control

A general process of utilising and/or producing substance, such as a(bio)therapeutic product, via cell culture may include one or more ofthe following steps:

1. Set up cell culture apparatus with suitable cell culture conditions;

2. Inoculate with cell population, e.g., starter culture grown onsmaller scale;

3. Allow cells to grow

4. If utilisation/production of substance is not automatic (e.g., ifdependent on a particular temperature or nutrient), adjust conditions toinduce utilisation/production;

5. Monitor culture conditions and adjust as required;

6. Monitor substance utilisation/production;

7. Harvest substance or breakdown product

-   -   from culture medium, if substance or breakdown product is        secreted    -   from cells, if substance or breakdown product accumulates within        cells        8. Purify substance or breakdown product, e.g., by removing any        contaminants.

During a process of utilising and/or producing a substance via cellculture, analysis may be carried out, e.g., via the method describedherein described herein, e.g., for monitoring the culture conditionsand/or substance utilisation/production. This may optionally involveobtaining a sample from the cell population for analysis. The skilledperson will be aware of suitable sample acquisition methods, such aspipetting, using a swab or the like. The sample may optionally beprocessed, e.g., a liquid sample may be filtered or processed togenerate a pellet as mentioned elsewhere herein. Optionally, a swab maybe used, which may optionally be analysed without further processingusing the method with a REIMS ion source.

Optionally, any adjustments to the culture conditions may be made, e.g.,if the analysis reveals the necessity for an adjustment. The culture pHmay be measured, e.g., with a pH meter, and optionally adjusted, e.g.,by adding an acid or a base as required. Nutrient use may be monitoredby analysing, e.g., respiration. The Respiratory Quotient, i.e. theratio of the Carbon Dioxide Evolution Rate to the Oxygen Uptake Rate,may be analysed. Metabolic products, e.g., the substance of interest, abreakdown product and/or contaminants, may be analysed.

Thus, optionally, the method may involve analysing, e.g., monitoring, aprocess of utilising and/or producing a substance via culture of a cellpopulation. Optionally, the invention provides a method of utilisingand/or producing a substance via culture of a cell population, whereinsaid method includes a step of analysing the utilisation and/orproduction process via a method of analysis of the invention.Optionally, said method further comprises a step of adjusting theculture conditions on the basis of the analysis.

Thus, the method may optionally comprise analysing the utilisationand/or production of a substance by a cell population. There is provideda method of producing a substance, comprising (a) using a first deviceto generate smoke, aerosol or vapour from a target in vitro or ex vivocell population and/or medium derived therefrom; (b) mass and/or ionmobility analysing said smoke, aerosol or vapour, or ions derivedtherefrom, in order to obtain spectrometric data; and (c) analysing saidspectrometric data in order to analyse the production of a substance bysaid target cell population. Also provided is a method of identifying acell population capable of utilising and/or producing a substance,comprising (a) using a first device to generate an smoke, aerosol orvapour from a target in vitro or ex vivo cell population and/or mediumderived therefrom; (b) mass and/or ion mobility analysing said smoke,aerosol or vapour, or ions derived therefrom, in order to obtainspectrometric data; and (c) analysing said spectrometric data in orderto identify a cell population capable of utilising and/or producing asubstance.

Any of these methods may optionally comprise analysing saidspectrometric data in order to (i) determine whether said cellpopulation utilises and/or produces said substance; (ii) determine therate at which said cell population utilises and/or produces saidsubstance; (iii) determine whether said cell population produces anyby-products; and/or (v) determine the mechanism by which said cellpopulation utilises and/or produces said substance. Optionally, two ormore cell populations may be analysed in order to determine which cellpopulation utilises and/or produces said substance at a higher rateand/or at a higher purity. Optionally, a plurality of cell populationsmay be analysed and divided into 2 or more subsets based on saidanalysis. For example, cell populations may be divided based on saidanalysis into subsets based on (i) their ability or inability to utiliseand/or produce said substance; (ii) the rate of utilisation and/orproduction of said substance; (iii) the production of any by-products;and/or (iv) the mechanism of utilisation and/or production. Optionally,based on the analysis cell populations may be divided into (i) a firstsubset capable of utilising and/or producing said substance and a secondsubset incapable of utilising and/or producing said substance; (ii) afirst subset and a second subset, wherein said first subset utilisesand/or produces the substance at a higher rate compared to the secondsubset; (iii) a first subset and a second subset, wherein said firstsubset produces no by-products, or fewer by-products compared to thesecond subset; and/or (iv) a first subset and a second subset, whereinsaid first subset utilises and/or produces the substance via a differentmechanism compared to the second subset.

Optionally, the method may further comprise a step of subjection thecell population or cell population subset to liquid chromatography massspectrometry (“LCMS”) analysis prior to and/or after the method ofanalysis. Optionally, based on said LCMS analysis, cell population maybe divided into subsets, or a said cell population subset as mentionedabove may be divided into further subsets.

Optionally, the cell population or cell population subset may becultured under conditions suitable to utilise and/or produce saidsubstance.

Optionally, the method does not comprise a step of subjection a cellpopulation, or cell population subset, to liquid chromatography massspectrometry (“LCMS”) analysis.

Optionally, the method may be used to monitor the utilisation and/orproduction of a substance, particularly to monitor the production ofby-products. This may involve analysing a sample from a cell populationat various time points, as discussed elsewhere herein.

Click Chemistry

“Click Chemistry” is a term that was introduced by K. B. Sharpless in2001 to describe reactions that are high yielding, wide in scope, createonly by-products that can be removed without chromatography, arestereospecific, simple to perform, and can be conducted in easilyremovable or benign solvents.

A typical click chemistry (click reaction) is the copper-catalyzed1,3-dipolar cycloadditions between azides and acetylenes.

A click reaction may, e.g., happen between a fluorescent probecomprising an alkyne and a biomolecule comprising an azide.

Thus, click chemistry may be used for attaching a probe or substrate ofinterest to a specific biomolecule, a process called bioconjugation. Thepossibility of attaching fluorophores and other reporter molecules hasmade click chemistry a very powerful tool for identifying, locating andcharacterizing both old and new biomolecules.

One of the earliest and most important methods in bioconjugation was toexpress a reporter on the same open reading frame as a biomolecule ofinterest. Notably, green fluorescent protein (“GFP”) is expressed inthis way at the N- or C-terminus of many proteins. However, thisapproach comes with several difficulties. For instance, GFP is a verylarge unit and can often affect the folding of the protein of interest.Moreover, by being expressed at either terminus, the GFP adduct can alsoaffect the targeting and expression of the desired protein. Finally,using this method, GFP can only be attached to proteins, and notpost-translationally, leaving other important biomolecular classes(nucleic acids, lipids, carbohydrates, etc.) out of reach.

To overcome these challenges, chemists have opted to proceed byidentifying pairs of bioorthogonal reaction partners, thus allowing theuse of small exogenous molecules as biomolecular probes. A fluorophorecan be attached to one of these probes to give a fluorescence signalupon binding of the reporter molecule to the target—just as GFPfluoresces when it is expressed with the target.

Optionally, the method provided herein may involve monitoring a clickchemistry reaction, e.g., to detect the end-products and/or anyby-products of a click chemistry reaction. Optionally, the method may beused in combination with click chemistry, e.g., before or after a clickchemistry reaction. Optionally, the method may be used instead of clickchemistry. For example, the method may allow the analysis of biomarkersthat would conventionally be analysed by using click chemistry, thusobviating the need for a click chemistry reaction.

Further Definitions

The method may be carried out on “target”, which may be a cellpopulation or a sample thereof and/or medium derived from a cellpopulation. The term “target entity” is used herein to refer to theentity which it is desired to analyse within the target. Thus, anyreference to a “target” should be understood to mean a target comprisingone or more different target entities. Thus, the target entity may,e.g., be a particular cell type, microbe and/or compound. Optionally,the target comprises only one type of target entities.

Any reference herein to “analysis”, “analysing” and derivatives of theseterms should be understood to be an analysis based on the spectrometricdata generated via the method provided herein.

The terms “analysis”, “analysing” and derivatives of these terms areused herein to encompass any of the following: detection of a targetentity or biomarker therefor; identification of a target or targetentity or biomarker therefor; characterisation of a target or targetentity or biomarker therefor; determination of a status, e.g., a diseasestatus, or biomarker therefor, and the like.

The analysis may be qualitative and/or quantitative. Thus, optionally,any type of analysis may involve determining the concentration,percentage, relative abundance or the like of the target entity. Forexample, the percentage of cells of one type within a cell population,and/or the concentration of a compound may be analysed. Optionally, anincrease or decrease in a target entity or biomarker therefor may beanalysed.

The terms “detection”, “detecting” and derivations of these terms areused interchangeably herein to mean that the presence or absence of atarget entity or biomarker therefor is determined.

The terms “identify”, “identification” and derivations of these termsare used interchangeably herein to mean that information about theidentity of a target, target entity or biomarker therefor is obtained.This may optionally be the determination of the identity, and/or theconfirmation of the identity. This may optionally include informationabout the precise identity of the target, target entity or biomarkertherefor. However, it may alternatively include information that allowsthe target entity to be identified as falling into a particularclassification, as discussed elsewhere herein.

By “identifying” a microbe is meant that at least some information aboutthe identity is obtained, which may, for example, be at any taxonomiclevel.

By “identifying” a cell is meant that at least some information aboutthe cell type is obtained. By “identifying” a diseased cell is meantthat it is determined or confirmed that a cell is diseased.

By “identifying” a compound is meant that at least some informationabout the structure and/or function of the compound is obtained, e.g.,the information may optionally allow a compound to be identified ascomprising or consisting of a compound selected from any of the typesdisclosed herein, and/or as being characterised by one or more of thefunctional groups disclosed herein.

The term “monitoring” and derivations of this term as used herein referto the determination whether any changes take place/have taken place.Typically, it is determined whether any changes have taken place overtime, i.e. since a previous time point. The change may, for example, bethe response to a substance, the production of a substance, and/or thedevelopment and/or progression of a disease, such as, any of thediseases mentioned herein. Optionally, the method may involve analysinga target and, on the basis of one or more of the following monitoring adisease: detecting a target entity; identifying a target entity;detecting an increase in a target entity; detecting a decrease in atarget entity.

The term “prognosis” and derivations of this term as used herein referto risk prediction of the severity of disease or of the probable courseand clinical outcome associated with a disease. Thus, the term “methodof prognosis” as used herein refers to methods by which the skilledperson can estimate and/or determine a probability that a given outcomewill occur. The outcome to which the prognosis relates may be morbidityand/or mortality. In particular, the prognosis may relate to“progression-free survival” (PFS), which is the length of time that asubject lives with the disease without the disease progressing. Thus,PFS may, for example, be the time from the start of therapy to the dateof disease progression, or the time from the end of therapy to the dateof disease progression.

Optionally, the method may involve analysing a target and, on the basisof one or more of the following making a prognosis: detecting a targetentity; identifying a target entity; detecting an increase in a targetentity; detecting a decrease in a target entity.

By “progressing” or “progression” and derivations of these terms ismeant that the disease gets worse, i.e. that the severity increases. Forexample, in the case of cancer, it may mean that the cancer becomesmalignant or more malignant.

The prognosis may relate to overall survival. By “overall survival” (OS)is meant the length of time that a subject lives with the disease beforedeath occurs. Overall survival may, for example, be defined as the timefrom diagnosis of the disease; the time of treatment start; or the timeof treatment completion, until death. Overall survival is typicallyexpressed as an “overall survival rate”, which is the percentage ofpeople in a study or treatment group who are still alive for a certainperiod of time after they were diagnosed with, or started treatment for,or completed treatment for, a disease. The overall survival rate may,for example, be stated as a five-year survival rate, which is thepercentage of people in a study or treatment group who are alive fiveyears after their diagnosis or the start or completion of treatment.

Statistical information regarding the average (e.g., median, mean ormode) OS and PFS of subjects having a particular type of disease isavailable to those skilled in the art. A determination whether a subjecthas, or is likely to have, an increased or decreased OS or PFS comparedto such an average may therefore be made.

A response to treatment may, e.g., be a decrease in the cell growthrate, a cessation of growth, and/or cell death. Alternatively or inaddition, a response to treatment may involve the appearance ordisappearance of one or more biomarkers, and/or an increase or decreasein the expression of one or more biomarkers.

The term “treatment” or “treating” as used herein refers to a course ofaction which is aimed at bringing about a medical benefit for a subject.The treatment may be prophylactic or therapeutic.

Optionally, the method may involve analysing a target and, on the basisof one or more of the following, determining that a subject should orshould not receive a particular treatment: detecting a target entity;identifying a target entity; detecting an increase in a target entity;detecting a decrease in a target entity.

Optionally, the method may involve analysing a target and, on the basisof one or more of the following, determining that a subject has or hasnot, or is likely to or not likely to, respond/responded a particulartreatment: detecting a target entity; identifying a target entity;detecting an increase in a target entity; detecting a decrease in atarget entity.

Optionally, the method may involve analysing a target and, on the basisof one or more of the following, administering a particular treatment toa subject: detecting a target entity; identifying a target entity;detecting an increase in a target entity; detecting a decrease in atarget entity.

Organoids

As mentioned above, the cell population may optionally be an organoid.

Mammalian tissues, particularly organs, are challenging to study as theyare typically only poorly accessible to experimental manipulation and/oranalysis. However, mammalian tissues may be generated ex vivo or invitro via three-dimensional (3D) culture techniques. This allows thereal-time study of such tissues, optionally including the independentmanipulation of various factors, e.g., genetic, and/ormicroenvironmental factors.

Tissue generated ex vivo or in vitro via a 3D culture technique arereferred to herein as “organotypic tissue” or “organoid”, terms whichare used interchangeably herein. Thus, these terms are used herein toencompass models of organs and/or models of tumours.

Organotypic tissue may be generated from organ “explants”, i.e. cells,or pieces of tissue, that were isolated from primary mammalian organs,or tumour explants, i.e. cells or pieces of tissue isolated from atumour. Explants may, e.g., be obtained from resected tissue and/orbiopsies. Organotypic tissue may alternatively be generated by expandingES cells, iPS cells or primary stem cells that have been purified fromorgans.

An organotypic tissue is a tissue which is formed in vitro and comprisesa collection of cells that form three-dimensional structures andresemble their in vivo counterparts. Unlike classical two-dimensionalcell cultures, organoid systems permit 3D cell growth, cell movement,cell differentiation and more physiologically representative cell-cellinteractions. In contrast to traditional 2D cell cultures, organoidsshare similar physical, molecular and physiological properties with thein vivo tissue of the same type. Organoids therefore represent effectivemodels for understanding organ development, tissue morphogenesis, and/orthe genetic or molecular basis of diseases. They have use indrug-screening, drug testing, and/or tissue replacement therapies.

Thus, the method may optionally comprise analysing an organoid. One ormore regions of an organoid may be analysed. Thus, the target may be anorganoid or a part thereof. Unless otherwise stated, any referenceherein to analysing an organoid should be understood to encompassoptionally analysing part of an organoid.

As mentioned elsewhere herein, the methods of the present inventionprovide spectrometric data on the cell types from which the aerosols aregenerated. Thus, the present methods permit biomarker analysis, e.g.,lipidomic fingerprinting, of cell types and/or, in the present context,organoid types.

It is within the competencies of one of ordinary skill in the art toprovide an organoid for their particular purposes. Methods for theproduction of organoids are well-known in the art. Optionally, themethod may comprise a step of producing an organoid.

By an “organoid” is meant an organised tissue formed in vitro or exvivo, said tissue having a cellular organisation and gross morphologysimilar to that of the same tissue type in vivo, for at least a subsetof the cells of the tissue. Such cellular organisation and grossmorphology is termed “in vivo-like gross and cellular morphology”herein.

An “organoid”, as used herein, may optionally include a scaffold whichis a pre-formed solid support that imparts or provides short-term (hoursto two weeks in culture) structure or support to the tissue or isrequired to form the tissue.

By “at least a subset of cells” is meant at least two cells, e.g., atleast 10%, 15%, 20%, 25%, 30%, 35%, 40%, or 50% of the cells of thetissue. Optionally, substantially all of the cells within the organoidhave a cellular organisation and gross morphology similar to that of thesame tissue type in vivo. By “substantially all of the cells” is meantat least 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% of the cells.

By “in vivo-like gross and cellular morphology” is meant athree-dimensional shape and cellular organisation substantially similarto that of the tissue in vivo.

By “three-dimensional” is meant an organised tissue having x, y and zaxes wherein x and y of the axes are at least 2 mm with z at least 0.05mm thick, and wherein 1, 2 or all of the axes are as great as 20 cm.Optionally, a three-dimensional tissue may be transferred between invivo and in vitro environments one or more times, e.g., exactly or atleast 2, 3, 4, 5, 6, 7, 8, 9 or 10 times without undergoing anysubstantial structural and/or shape changes.

The organoid may be any type of organoid, e.g., an organoid of any ofthe cell and/or tissue types mentioned elsewhere herein. Optionally, theorganoid may be selected from, e.g., cerebral organoids, thyroidorganoids, intestinal organoids (e.g., small intestinal, colonic,rectum, duodenum or ileum organoids), testicular organoids, hepaticorganoids, pancreatic organoids, gastric organoids, epithelialorganoids, lung organoids, kidney organoids, muscle organoids, prostateorganoids, breast organoids, blood vessel organoids, lymphatic vesselorganoids and/or retinal organoids.

Optionally, the organoid may be prepared from terminally differentiatedcells of the desired organ, e.g., an “intestinal organoid” may bederived from terminally differentiated intestinal cells. However, moretypically, organoids may be produced from stem cells. Optionally, theorganoid may be prepared from an explant or cells from a patient.

The term “similar to that of the same tissue type in vivo” means thatthe organoid has genetic and phenotypic characteristics that allow it tobe recognised by the skilled person as being from or associated with aparticular tissue type (such as the tissues listed above). It does notmean that the organoid necessarily has to be genetically andphenotypically identical to the corresponding in vivo tissue type.

An organoid that has the in vivo genotype and phenotype of the intestineis, for the purposes of this invention, comprised within the definitionof an “intestinal organoid”. The same applies mutatis mutandis for theother organoid types listed above.

Optionally, the organoid may be a mammalian organoid, i.e. it may bederived from cells or an explant taken from a mammal. The mammal may beany mammal of interest, e.g., selected from the mammals listed elsewhereherein. Optionally, the organoid may be a non-human organoid.Optionally, the organoid may be a human organoid.

Optionally, the organoid may be diseased, e.g., cancerous. Optionally,it may have been generated from a diseased explant or cells. Optionally,it may have been generated from a healthy explant or cells. Optionally,a disease state may be induced in the organoid, e.g., via geneticmanipulation and/or via the use of a disease-causing agent.

Optionally, the genotype and/or phenotype of the explant and/or cellsmay be altered prior to the generation of an organoid. Optionally, thegenotype and/or phenotype of the organoid may be altered during or afterthe generation of the organoid. Optionally, the genotype and/orphenotype of a mature organoid may be altered.

The manipulation of the genotype and/or phenotype may optionally beachieved via any of the genotype and/or phenotype manipulation methodsmentioned elsewhere herein.

The term “mature organoid” as used herein means an organoid in whichsubstantially all of the cells within the organoid are differentiated,optionally terminally differentiated. By “differentiated” is meant cellswith numerous mature-like characteristics, either chemical or physical.By “terminally differentiated” is meant is not capable of furtherproliferation or differentiation into another cell or tissue type.

An organoid may comprise a single cell type. More typically, an organoidmay comprise a mixture of different cell types.

Organoids are typically sufficiently small that they can be sustainedwithout a blood supply. Therefore, optionally the organoid herein lacksvasculature. Recent developments in organoid production technology haveresulted in organoids comprising vasculature. Thus, optionally theorganoid herein comprises vasculature.

The organoid may, e.g., be a free-floating multicellular sphere,optionally in which highly polarized cells localize around a centrallumen.

In the methods of the present invention, the organoid which is analysedmay exist in vitro or ex vivo, or it may be in vivo in that it may havepreviously been transplanted into a subject. Optionally, the method mayinclude a step of transplanting an organoid into a subject. Thetransplant may optionally be orthotopical, i.e. into the organ oforigin, e.g., an organoid generated from a diseased explant or diseasedcells may be transplanted orthotopically and the interaction between theorganoid and the organ of origin may be analysed.

The organoid which is analysed may be a specimen of an organoid that haspreviously been implanted into a subject, said specimen havingsubsequently been removed from the subject. The subject may be anysubject of interest, e.g., selected from any of the subjects listedelsewhere herein.

The provision of spectrometric data from a particular organoid (herein a“target organoid”) may be useful in itself. Such data may be analysed,e.g., by using reference spectrometric data, as discussed elsewhereherein. Alternatively or in addition, information can be obtained from acomparison of the spectrometric data obtained from the target organoidwith the spectrometric data obtained from one or more other organoids(herein “comparator organoids”).

The skilled person is well aware of the structure of an organoid of agiven organ and knows how to produce said organoid. Optionally, themethod may include one or more steps of producing an organoid, whichmay, e.g., be selected from the following steps:

1. Obtain suitable cells, e.g., from a cell bank, a patient, or generatede novo:

2. Set up culture apparatus to required conditions;

3. Seed cells within culture apparatus and allow to grow;

4. Monitor culture conditions and adjust as required; and

5. Analyse organoid or a part thereof.

Any of the steps may optionally be performed as many times as necessaryor desired, e.g., at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 times. Steps3 can be performed as many times as necessary, wherein the same and/ordifferent cell types may be added to the culture apparatus each time thestep is performed. Step 3 may be performed before or after step 4, orsimultaneously with step 4. If step 3 is performed multiple times, eachperformance may separately be performed before or after step 4, orsimultaneously with step 4.

Step 5 may be carried out repeatedly at suitable intervals to monitorthe organoid. If the analysis in step 5 reveals it to be necessary, step3 and/or step 4 may optionally be repeated once or more as describedabove.

In step 1, the cells are optionally stem cells, i.e. cells that are notterminally differentiated. The cells may be totipotent stem cells,pluripotent stem cells, multipotent stem cells or oligopotent stemcells. Alternatively, they may be unipotent cells. The term “stem cell”as used herein encompasses totipotent, pluripotent, multipotent andoligopotent stem cells. Optionally, the stem cells are embryonic stemcells, fetal stem cells, adult stem cells, amniotic stem cells orinduced pluripotent stem cells (iPSCs).

Totipotent stem cells are those can differentiate into embryonic andextraembryonic cell types. Pluripotent stem cells, such as embryonicstem cells and iPSCs are those that can differentiate into any cellderived from any of the three germ layers. A multipotent cell is a cellwhich has the potential to give rise to cells from multiple, but alimited number of lineages. An example of a multipotent cell is ahematopoietic cell, a blood stem cell that can differentiate intoseveral types of blood cells, but cannot develop into brain cells orother types of cells. Mesenchymal stem cells, or MSCs, are multipotentstem cells that can differentiate into a variety of cell types includingosteoblasts (bone cells), chondrocytes (cartilage cells) and adipocytes(fat cells). Oligopotent stem cells can differentiate into only a fewcell types; examples include lymphoid and myeloid stem cells. Unipotentcells can produce only one cell type, their own, but have the propertyof self-renewal, which distinguishes them from other non-stem cells suchas progenitor cells. An induced pluripotent stem cell (abbreviated asiPSC or iPS cell) is a type of pluripotent stem cell artificiallyderived from a non-pluripotent cell, typically an adult somatic cell, byinducing a “forced” expression of certain genes.

Alternatively, the cells may be non-stem cells, e.g., progenitor cellsor terminally differentiated cells with no differentiation potential.

Optionally, in step 1, tissue or cell explants may be used, which mayoptionally be diseased, e.g., cancerous.

In some embodiments, the cells obtained in step 1 are a population ofcells with different differentiation potentials.

The skilled person will be aware of the particular cell type to obtainfor their particular desired purposes.

Various methods and apparatuses for culturing organoids are known in theart (see for instance Shamir and Ewald, Nature Reviews Molecular CellBiology 15, 647-664 (2014))

Step 2 optionally comprises mixing disaggregated or partiallydisaggregated cells with a solution of extracellular matrix componentsto create a suspension. By “extracellular matrix components” is meantcompounds, whether natural or synthetic compounds, which function assubstrates for cell attachment and growth.

The composition of the solution of extracellular matrix components mayvary according to the tissue to be produced. Representativeextracellular matrix components include, but are not limited to,collagen, laminin, fibronectin, vitronectin, elastin,glycosaminoglycans, and/or proteoglycans, and/or combinations of some orall of these components. An optional solution is Matrigel, which is agelatinous protein mixture secreted by Engelbreth-Holm-Swarm (EHS) mousesarcoma cells produced and marketed by Corning Life Sciences. Matrigelresembles the complex extracellular environment found in many tissuesand is used by cell biologists as a substrate for culturing cells.

The culture medium may be undefined or defined and optionally beserum-free. Suitable culture conditions, media and nutrients arediscussed elsewhere herein.

Step 3 optionally comprises placing the tissue explant or cells, e.g.,the above-mentioned suspension, in a vessel having a three dimensionalgeometry which approximates the in vivo gross morphology of the tissue.The cells and any extracellular matrix components are optionally thenallowed to coalesce or gel within the vessel. Optionally, the vessel isplaced within a culture chamber and surrounded with media underconditions in which the cells are allowed to form an organized tissue.

The vessel may optionally comprise tissue attachment surfaces. By“tissue attachment surfaces” is meant surfaces having a texture, chargeor coating to which cells may adhere in vitro. Examples of attachmentsurfaces include, without limitation, stainless steel wire, VELCRO,suturing material, native tendon, covalently modified plastics andsilicon rubber tubing having a textured surface.

In general, the vessel containing the organoid may be placed in astandard culture chamber (e.g., wells, dishes, or the like), and thechamber may then be filled with culture medium until the vessel issubmerged. The precise composition of the culture medium will howevervary according to the tissue to be produced, the necessity ofcontrolling the proliferation or differentiation of some or all of thecells in the tissue, the length of the culture period and therequirement for particular constituents to mediate the production of aparticular bioactive compound. The culture vessel may be constructedfrom a variety of materials in a variety of shapes as described.

Step 4 above may require the replenishment, addition or removal of oneor more culture media components.

Culture media may comprise any combination of components that permitsmaintenance of the organoid tissue. An exemplary medium for long termviability of an organoid consists of DMEM with high glucose, 10% horseserum, 5% fetal calf serum, and 100 units/ml penicillin.

Optionally, at any stage of the organoid production process, there is astep of engrafting mature organoids into a host animal, which may e.g.,be selected from any of the animals mentioned elsewhere herein, to allowthem to further develop.

Step 5 above is a step of analysing an organoid. The results of theanalysis may indicate that step 3 and/or step 4 should be repeated oneor more times, optionally with modification.

The methods of the present invention may comprise obtainingspectrometric data from one or more locations of a target organoid andanalysing said spectrometric data.

If the target is an organoid, then the method may optionally be used todetermine one or more properties of said organoid. Analysis using themethod provided herein in step 5 above provides particular advantages inthe organoid production process; namely the rapid obtaining of theresults of an analysis of one or more of the organoids' properties.

Optionally, the property of the organoid that is analysed is a propertyof the organoid as a whole. Alternatively, the property of the organoidthat is analysed is a property of a region within said organoid. Thus,the methods of the invention can be performed on a chosen or randomlyselected region of an organoid.

Optionally, the property analysed is the cell type of which the organoidor region thereof is comprised. When producing organoids, it isimportant to ensure that the desired cell type(s) is/are located withinthe desired regions of the organoid. This is particularly the case inthe production of organoids from stem cells and the production oforganoids that comprise multiple cell types. The production of organoidsfrom stem cells requires controlled differentiation of stem cells intospecific desired cell type(s), potentially in only certain regions ofthe organoid. Differentiation of stem cells into an off-target cell typewould lead to the production of an organoid that less accuratelyrepresented the corresponding in vivo organ tissue. Different cell typeswill be distinguishable by characteristic spectrometric data. Thus, themethod may be used to identify one or more cell types present in anorganoid or a region thereof; details of cell identification areprovided elsewhere herein. If the analysis reveals that an undesiredcell type is present, then the production process can be re-started, orthe undesired cells excised from the organoid or contacted with one ormore agents that induce apoptosis.

As mentioned above, organoids may optionally be produced from stemcells. In such embodiments, optionally the property that is analysed isthe differentiation potential of the cells in the organoid or a regionthereof. The differentiation potential of cells in the organoid shouldreduce over time during the organoid production process, so theirdifferentiation status should change. Therefore, of particular interestto the skilled person producing a particular organoid is the timing ofcell differentiation. The differentiation potential or status is aphenotypic and/or genotypic property of a cell, which may be analysed bythe method provided herein as discussed elsewhere herein. The skilledperson may need to add to or remove from the culture media factors thatinfluence cell differentiation at specific time points.

This aspect of the organoid production process can be analysed using themethod provided herein. Cells with differing differentiation potentials,e.g., pluripotent, multipotent, oligopotent, progenitor and/or fullydifferentiated cells, will be distinguishable by characteristicspectrometric data. If the analysis reveals that cells within theorganoid have an undesirable differentiation potential, thenameliorative steps can be taken. Such steps are optionally the excisionof the undesirable cells and/or application of one or more agents toinduce or deplete differentiation potential as required.

Thus, the methods of the present invention permit, e.g., rapid analysisof the growing tissue during organoid production processes to ensurethat it comprises the desired cell types, does not comprise undesiredcell types and/or comprises the desired cell types at the desiredpositions within the organoid structure. The analysis method providedherein permits the skilled person to modify the culture conditions ofthe organoid during and/or after the development process, therebyincreasing the likelihood that organoids of the desired type will beproduced and/or maintained.

Optionally, the property analysed is the phenotypic and/or genotypiccondition of the cells of the organoid or the cells of a region of theorganoid. Optionally, by phenotypic and/or genotypic condition is meantthe viability of the cells therein, the disease state of the cellstherein, and/or the level of oxidative damage/stress within the cellstherein, particularly the level of lipid oxidation. Cells havingdifferent phenotypic and/or genotypic conditions will be distinguishableby characteristic spectrometric data. Thus, the phenotype and/orgenotype of the organoid may be analysed by the method provided herein,as discussed elsewhere herein.

Optionally, the method may involve the analysis of the effect ofenvironmental conditions on an organoid, details of which are discussedelsewhere herein. After analysis of any property of a target organoid asdescribed herein, one or more of these conditions may optionally beappropriately modified. Such modification is within the competencies ofone of ordinary skill in the art.

Optionally, if an undesirable disease state is detected, for instanceinfection of the cells of the organoid by an infectious agent, thensteps can be taken to remove the infection through treatment withappropriate agents, and/or through excision of the infected cells.

Once produced, organoids can be maintained in culture. However,different organoid types degrade, i.e. lose functionality and/or optimalcondition over time. This is particularly problematic in certainorganoid types, e.g., neurons in cerebral organoids decline and arereplaced by glia by 15 months after production.

The present methods permit analysis of organoids as described above notonly during the organoid production process, but also once the organoidhas been produced. In other words, the methods permit analysis of amature organoid or a region thereof. The cell type, migration,differentiation potential, and/or condition of cells of a matureorganoid or a region thereof may also be analysed. Spectrometric dataobtained from the target organoid or region thereof may be compared tospectrometric data from the same organoid or region thereof at anearlier time point to determine changes in properties of the organoidover time. Thus, the method may optionally be used to monitor anychanges in the properties of an organoid.

In certain instances it may be desirable to prepare an organoid that isa model of a particular disease. Organoids may be produced that exhibitdisease specific phenotypes. Such organoids have great use in the studyof diseases and the study of potential therapies, including, but notlimited to, their use in methods of screening agents for therapeuticefficacy.

The preparation of organoids with disease specific phenotypes is withinthe competencies of one of ordinary skill in the art. Optionally, thedisease state is induced through genetic modification of the cells fromwhich the organoid is produced, or through application of one or moredisease-inducing agents to the organoid. Optionally the disease state iscancer, an infection, and/or a disease selected from any of the diseasesdisclosed elsewhere herein.

The method may optionally comprise the step of analysing the diseasestate of the organoid or a region thereof. Analysing the disease statemay comprise analysing the incidence of, severity of, or progression ofa disease state. Cells of differing disease states will result incharacteristic spectrometric data.

Optionally, the analysis of the disease state may occur once or moreduring the production of an organoid with a disease state phenotype. Inthis way, generation of the disease state phenotype can be monitored andencouraged or discouraged as desired.

Optionally, the analysis of the disease state is performed on matureorganoids.

Optionally, the methods may comprise analysis of the disease states oforganoids with different genotypes and/or phenotypes to reveal the roleof specific genotypes and/or phenotypes in the incidence, severityand/or progression of diseases.

The method may optionally comprise analysing the disease state of anorganoid produced following administration of a candidate compound,wherein said candidate compound prevents, treats, induces, and/orenhances the disease state in question. In this way, the methods of theinvention permit the use of organoids in compound screens.

Thus, the present invention also provides a compound screening methodcomprising the steps of:

-   -   i) contacting an organoid with a candidate compound;    -   ii) obtaining spectrometric data from said organoid or a region        thereof as described elsewhere herein; and    -   iii) analysing said data to determine one or more properties of        said candidate compound.

The organoid may be an organoid as described anywhere else herein.Optionally, the organoid or region thereof displays a disease stategenotype and/or phenotype. Typically, the property to be analysed is theeffect of the compound on the disease state of the organoid.

Candidate compounds that may be screened optionally include but are notlimited to toxins, cytokines, neurotransmitters, growth factors,morphogens, inhibitors, stimulators, bacteria, viruses, DNA, anti-sensenucleic acids, drugs, peptides, natural compounds, and/or any of theother compounds listed elsewhere herein.

Optionally the methods comprise analysing the disease state of theorganoid following genetic modification thereof, for instance via genomeediting techniques.

Optionally, the organoid is present in a kit that comprises a plurality(i.e., at least 6, optionally 24, 48, 96, and even up to severalthousand) of organized tissues individually contained in a container.

The generation and/or analysis of organoids will now be described byreference to specific embodiments, but it should be understood that theprinciples described below apply mutatis mutandis to the generationand/or analysis of any type or organoids.

Optionally, the organoid may be sectioned and/or sequentiallydisassociated, e.g., mechanically and/or enzymatically, for example withtrypsin, to obtain different layers of an organoid, and/or to derivecells from different layers of an organoid. Different layers, or cellsderived from different layers, of the organoid may then be analysed.During organoid growth and/or maintenance, different layers of anorganoid may have been exposed to different environmental conditions,and/or been exposed to different concentrations of a substance, assubstances may not penetrate each layer at the same rate. Thus, themethod may optionally be used to analyse one or more different layers ofan organoid, or cells derived therefrom.

Human Colon Carcinoma Organoids

Human colon carcinoma LIM1863 cells may be grown as free-floatingmulticellular spheres (organoids) in which highly polarized cellslocalize around a central lumen. These organoids resemble colonic cryptsin that they contain morphologically differentiated columnar and gobletcells. LIM1863 cells may be cultured in RPMI 1640 medium containing 5%FCS, α-thioglycerol (10 μm), insulin (25 units/l), and hydrocortisone (1mg/L), with 10% CO₂ at 37° C.

The LIM1863 cells (6×10⁸ cells) may be washed four times with 30 ml ofRPMI 1640 medium and cultured for 24 h in 150 ml serum-free RPMI mediumsupplemented with 0.6% insulin-transferrin-selenium solution.

Culture medium (CM) may be collected and centrifuged at 4° C. (480×g for5 min followed by 2,000×g for 10 min) to remove intact cells and celldebris. CM may be centrifuged at 10,000×g for 30 min to isolate sMVs.

According to an embodiment CM may be filtered using a VacuCap® 60 filterunit fitted with a 0.1 μm Supor® Membrane and then concentrated to 500μl using an Amicon® Ultra-15 Ultracel centrifugal filter device with a5K nominal molecular weight limit.

Pancreatic Cancer Organoids

Pancreatic cancer is one of the most lethal malignancies due to its latediagnosis and limited response to treatment. Tractable methods toidentify and interrogate pathways involved in pancreatic tumorigenesisare desired. Pancreatic organoids can be rapidly generated from resectedtumours and biopsies, survive cryopreservation, and exhibit ductal- anddisease-stage-specific characteristics. Orthotopically transplantedneoplastic organoids recapitulate the full spectrum of tumourdevelopment by forming early-grade neoplasms that progress to locallyinvasive and metastatic carcinomas. Due to their ability to begenetically manipulated, organoids are a platform to probe geneticcooperation. Comprehensive transcriptional and proteomic analyses ofmurine pancreatic organoids have revealed genes and pathways alteredduring disease progression. The confirmation of many of these proteinchanges in human tissues demonstrates that organoids are a useful modelsystem to discover important characteristics of pancreatic cancer.

Human Mammary Explant Culture

The mammary explant culture system and mammary epithelial cell culturesystem provide useful in vitro models to examine the responsive ness ofnoncancerous mammary tissue to agents that affect cell proliferation,cytodifferentiation and neoplastic transformation at the molecular,biochemical and cellular levels. The tissue culture technology andbiomarker assays established for the murine models have been optimizedfor human mammary tissue.

Explant cultures may be prepared from human mammary terminal ductlobular unit (TDLU) obtained from surgical samples. The TDLU are theendocrine responsive and proliferatively active intact organoids thatrepresent target tissue for carcinogenesis.

According to an embodiment these organoids may be maintained in achemically defined, serum-free Waymouth's MB 752/1 medium supplementedwith 5 pg/ml insulin, 1 ng/ml E2, 2 mM L-glutamine and antibiotics.

The medium may be routinely changed every 48 hr and the cultures may bemaintained in a humidified atmosphere of 95% air: 5% CO2 at 37° C.

According to an embodiment the human mammary epithelial 184-E5 cell linemay be maintained in chemically defined, serum-free KBM-MEM mediumsupplemented with 10 pg/ml insulin, 10 ng/ml epidermal growth factor, 10pg/ml transferrin, 0.5 pg/ml hydrocortisone, and 5 pg/ml gentamycin(24,25).

The medium may be routinely changed every 48 hr and the cells may besubcultured by a 1:4 split when approximately 70% confluent.

Xenografts

Cells and/or tissue may optionally be xenografted into a host organismfor a suitable period of time, e.g., at least 1, 2 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 2, 23, or 24 hoursand/or 1, 2 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 2, 23, or 24 days. For example, cells or tissue obtained from ahuman tumour may be xenografted into a host animal. Optionally, themethod may involve making a xenograft and/or removing a xenograft orsample thereof from a host organism. Optionally, the method may beperformed on a provide xenograft.

Optionally, the xenograft may comprise or consist of tumour cells.Optionally, cells may be obtained from a xenograft to establish axenograft-derived cell population. A xenograft-derived cell population,may optionally be analysed, e.g., to analyse the impact of the hostenvironment on the cell population. Optionally, a cell population may beanalysed prior to an after xenografting, and/or a xenograft-derivedpopulation may be compared to a cell population that is notxenograft-derived.

Imaging

According to the various embodiments herein, ion imaging may be used togenerate an image or map of one or more properties of the target, e.g.,if the target is an organoid. This may be achieved by using the firstdevice to generate aerosol, smoke or vapour from multiple differentregions of the target; ionising analytes in the smoke, aerosol or vapouroriginating from the different regions to produce analyte ions (or ionsderived therefrom, e.g., fragment ions); and then analysing the analyteions (or ions derived therefrom) to obtain spectrometric data for eachof the regions of the target. The spectrometric data is correlated tothe region of the target to which it relates (i.e. from where the smoke,aerosol or vapour that generated the spectrometric data originated from)so as to generate image or map data. An image or map of the target canthen be generated based on the image or map data. For example, one ormore properties of each region of the target may be determined from thespectrometric data and this may be included in the image or map data andhence mapped as a function of location within the target. The image ormap data may then be displayed to a user.

The first device may be stepped between multiple spaced apart regions ofthe target so as to generate the aerosol, smoke or vapour from discreteregions of the target. Alternatively, a plurality of devices may be usedto generate the aerosol, smoke or vapour from discrete regions of thetarget, optionally simultaneously. These plurality of devices may notmove across the target, although may move into and out of engagementwith the target. Alternatively, the first device may be moved across orthrough the target continuously so as to generate aerosol, smoke orvapour from the different regions of the target. Any movements of thefirst device, or the plurality of devices, may be automated andcontrolled by a machine.

The spectrometric data for each region may be analysed and convertedinto data representative of the type, condition or constituent(s) of thematerial at that region in the target.

The representative data may then be displayed as an image or map showingthe type, condition or constituents of the material as a function oflocation in the target.

For example, the representative data may indicate the type, level,presence and/or absence of: diseased; cancerous; and/or necroticmaterial at each of the regions in the target. For example, thespectrometric data may be used to identify and/or display the locationsof margins of diseased, cancerous, and/or necrotic tissue in the target.These tissue types, such as tumour tissue, may closely resemble normaltissue and may have indistinct boundaries, making it difficult todetermine where the tumour ends and the normal tissue begins. The methodprovided herein enables the locations of such tissue margins to beidentified.

Additionally, or alternatively, the spectrometric data may be used toidentify and/or display the location and/or margins of one or more cellor tissue type of interest. For example, the cell or tissue type ofinterest may comprise diseased and/or cancerous and/or necrotic tissueor cells in the target; and/or the cell or tissue type of interest maycomprise healthy tissue or cells.

The representative data may indicate the different type of cells orconstituents in the target.

Additionally, or alternatively, the representative data may indicate thepresence and/or distribution of one or more types of microbes within thetarget.

Additionally, or alternatively, the representative data may indicate thepresence and/or distribution of one or more types of compounds withinthe target.

Additionally, or alternatively, the representative data may indicate thetype or level of biomarker in the target, and the distribution of thetype or level of biomarkers within a target may be identified and/ordisplayed.

The ion imaging and map data may be generated and/or displayed inreal-time.

Ion imaging mass spectrometry technology, such as DESI-MS and/or REIMStechnology, may optionally be used to obtain the spectrometric data forthe different regions of the target. A REIMS technology device mayoptionally be used in cutting and/or pointing mode.

Some ion imaging mass spectrometry technology, such as DESI-MS, does notdestroy the entirety of an organoid, so such a technology may optionallybe used to analyse an organoid, wherein at least some cells of theorganoid survive the method of analysis.

This ion imaging analysis may optionally be combined with a furtheranalysis of the specimen. Details of further analysis methods and toolsare provided elsewhere herein. Optionally, the results of massspectrometry imaging may be correlated with the results of a furtheranalysis.

More details as to how to perform ion imaging are discussed below withreference to a particular example of DESI imaging. It will be understoodthat the specific parameters discussed were those used in an assay bythe inventor, and that any of these parameters may be varied.

Specimens, such as infected tissue sections or cell cultures, smearedonto the surface of a standard glass microscope slide, were subjected toDESI-MS imaging analysis using an Exactive mass spectrometer (ThermoFisher Scientific Inc., Bremen, Germany) Exactive instrument parametersare listed in the Table below.

Thermo Exactive instrumental parameters used for DESI-MS imaging.

Parameter Setting. Polarity negative Resolution 100,000 Mass range200-1050 Spray voltage −4.5 kV Capillary temperature 250° C. Capillaryvoltage −50 V Tube lens voltage −150 V Skimmer Voltage −24 V Max.injection time 1000 ms Microscans 1 AGC target 5e6

Methanol/water (95:5 v/v) was used as the electrospray solvent at aflow-rate of 1.5 μL/min. Nitrogen N4.8 was used as nebulising gas at apressure of 7 bars. All solvents used were of LC-MS grade (Chromasolv,Sigma Aldrich, St Louis, Mo., USA). The height distance between the DESIsprayer and the sample surface was set to 2 mm with the distance betweenthe sprayer and sniffer set to 14 mm. The distance between the samplesurface and the inlet capillary of the mass spectrometer was <<1 mm. Theangle between the sprayer tip and the sample surface was set at 80°. Thecollection angle between inlet capillary and sample was set to 10°.

The general principle underlying imaging processes using DESI MS is thatrather than point-by-point sampling, horizontal line scans are performedover the specimen surface by moving the automated sampling platform at aspeed that covers the area determined as a pixel (spatial resolution) inthe time the mass spectrometer requires to complete one scan (acquireone mass spectrum). This results in each one file per row of theresulting image (number of rows determined by sample height divided byspatial resolution).

For image analysis, individual horizontal line scans were converted into.imzML files using the imzML Converter Version 1.1.4.5(www.maldi-msi.org). Single ion images and RGB images were generatedusing MSiReader Version 0.05 (146) with linear interpolation (order 1)and 0.005 Da bin size.

Biomarkers

The method may optionally involve the analysis of one or morebiomarkers. A biomarker may be an objective, quantifiable characteristicof, e.g., a cell type, disease status, microbe, compound, and/orbiological process.

The term “biomarker” is sometimes used explicitly herein, but it shouldalso be understood that any of the analyses mentioned herein mayoptionally be the analysis of a biomarker. Thus, e.g., any reference toanalysing a “cell type” should be understood optionally to be “analysinga biomarker for a cell type”; any reference to analysing a “phenotypeand/or genotype” should be understood optionally to be “analysing aphenotype and/or genotype biomarker”; and so on.

The biomarker may optionally be a spectrometric biomarker. The term“spectrometric biomarker” is used herein to refer to spectrometric datathat is characteristic of a cell type, disease status, microbe,compound, and/or biological process, but for simplicity, a spectrometricbiomarker may simply be referred to as a “biomarker”.

By “characteristic of a cell type” is meant that the biomarker mayoptionally be used to analyse, e.g., detect, identify and/orcharacterise said cell type. Optionally, the biomarker may be used todistinguish between cells originating from different tissues; betweengenotypically and/or phenotypically different cell types; between ananimal cell and a microbial cell; between a normal and an abnormal cell;between a wild-type and a mutant cell; and/or between a diseased and ahealthy cell.

By “characteristic of a disease status” is meant that the biomarker mayoptionally be used to analyse the disease status of a target.Optionally, the biomarker may be used to distinguish between healthy anddiseased cells; and/or to analyse the severity, grade, and/or stage of adisease.

By “characteristic of a microbe” is meant that the biomarker mayoptionally be used to analyse, e.g., detect, identify and/orcharacterise said microbe. As discussed elsewhere herein, identificationmay be on any level, for example, on a taxonomic level. A biomarker thatallows identification of a microbe as belonging to a particulartaxonomic level may be referred to as a “taxonomic marker” or “taxonomicbiomarker”. Thus, a taxonomic marker may be specific for a Kingdom,Phylum, Class, Order, Family, Genus, Species and/or Strain.

By “characteristic of a compound” is meant that the biomarker mayoptionally be used to analyse, e.g., detect, identify and/orcharacterise said compound.

By “characteristic of a biological process” is meant that the biomarkermay optionally be used to analyse a biological process. Optionally, thebiomarker may be used to analyse the start, progression, speed,efficiency, specificity and/or end of a biological process.

Different cell types, disease states, compounds, microbes, biologicalprogresses and the like may be characterised by the presence or absence,and/or relative abundance, of one or more compounds, which may serve asbiomarkers. Any reference herein to a biomarker being a particularcompound, or class of compounds, should be understood optionally to bethe spectrometric data of that compound, or class of compounds.

For example, a reference to a “C24:1 sulfatide (C48H91NO11 S)” biomarkershould be understood to be a reference to the spectrometric datacorresponding to C24:1 sulfatide (C48H91 NO11 S) which may, e.g., be asignal corresponding to m/z of about 888.6; whereas a reference to a“glycosylated ceramide” biomarker should be understood to be a referenceto the spectrometric data corresponding to glycosylated ceramide, whichmay, e.g., be a signal corresponding to m/z of 842, 844 or 846.

As explained above, a biomarker may be indicative of a cell type,disease status, microbe, compound, and/or biological process. Abiomarker which is indicative of cancer may therefore be referred to asa “cancer biomarker”; a biomarker which is indicative of Pseudomonasaeruginosa may be referred to as a “Pseudomonas aeruginosa biomarker”and so on.

Optionally, a spectrometric biomarker may be identified as being thespectrometric data of a particular compound, or class of compounds.Thus, a signal corresponding to a particular mass, m/z, charge stateand/or ion mobility (e.g., due to cross-sectional shape or area) mayoptionally be identified as being indicative of the presence of aparticular compound, or class of compounds.

Optionally, a spectrometric signal may serve as a biomarker even if adetermination has not been made as to which particular compound, orclass of compounds gave rise to that signal. Optionally, a pattern ofspectrometric signals may serve as a biomarker even if a determinationhas not been made as to which particular compounds, or class ofcompounds, gave rise to one or more signals in that pattern, or any ofthe signals in a pattern.

The work disclosed herein has led to the identification of a range ofbiomarkers, as well as allowing the identification of furtherbiomarkers. Optionally, the biomarker may be selected from any of thebiomarkers disclosed herein, including in any of the Examples and/orTables. Optionally, the biomarker may be a biomarker of the substitutedor unsubstituted form of any of the biomarkers mentioned herein; and orof an ether, ester, phosphorylated and/or glycosylated form, or otherderivative, of any of the biomarkers mentioned herein.

Optionally, the biomarker may be a biomarker of a lipid; a protein; acarbohydrate; a DNA molecule; an RNA molecule; a polypeptide, such as, aribosomal peptide or a non-ribosomal peptide; an oligopeptide; alipoprotein; a lipopeptide; an amino acid; and/or a chemical compound,optionally an organic chemical molecule or an inorganic chemicalmolecule.

A biomarker may optionally be the clear-cut presence or absence of aparticular compound, which may optionally manifest itself as thepresence or absence of a spectrometric signal corresponding to aspecific mass, charge state, m/z and/or ion mobility.

A biomarker may optionally be the relative abundance of a particularbiomolecule or compound, which may optionally manifest itself as therelative intensity of a spectrometric signal corresponding to a specificmass, charge state, m/z and/or ion mobility

A biomarker may optionally be the relative abundance of more or morecompounds, which may optionally manifest itself as the relativeintensity of two or more spectrometric signals corresponding to two ormore mass, charge state, m/z and/or ion mobility.

Thus, a biomarker may optionally be an increased or decreased level ofone or more compounds, e.g., a metabolite, a lipopeptide and/or lipidspecies, which may optionally manifest itself as an increase and/ordecrease in the intensity of two or more spectrometric signalscorresponding to two or more mass, charge state, m/z and/or ion mobility

The presence, absence and relative abundance of a variety of compoundsmay be referred to as a molecular “fingerprint” or “profile”. Thetotality of the lipids of a cell may be referred to as a lipidomicfingerprint/profile, whereas the totality of metabolites produced by acell may be referred to as a metabolomic fingerprint/profile.

Thus, the biomarker may be a molecular fingerprint, e.g., a lipidfingerprint and/or a metabolomic fingerprint, more particularly e.g., a(i) a lipidomic profile; (ii) a fatty acid profile; (iii) a phospholipidprofile; (iv) a phosphatidic acid (PA) profile; (v) aphosphatidylethanolamine (PE) profile; (vi) a phosphatidylglycerol (PG)profile; (vii) a phosphatidylserines (PS) profile; or (viii) aphosphatidylinositol (PI) profile.

By way of example, phosphatidylglycerol may be found in almost allbacterial types, but it may be present in different bacteria indifferent relative amounts. Phosphatidylglycerol may be present at alevel of only 1-2% in most animal tissues. It may therefore be abiomarker for bacteria in an animal specimen, and/or be a biomarker forspecific types of bacteria.

The biomarker may optionally be a direct biomarker or an indirectbiomarker. By “direct” biomarker is meant that the spectrometric data isproduced directly from the biomarker. For example, if a particularcompound has a specific spectrometric signal or signal pattern, thenobtaining this signal or signal pattern from a sample provides directinformation about the presence of that compound. This may be the case,for example, for a metabolite produced in significant amounts by a cellor microbe. Optionally, in such an example, the spectrometric data fromthe compound may alternatively or in addition serve as an indirectbiomarker for the cell or microbe that produced this compound.

By “indirect” biomarker is meant that the spectrometric data is producedfrom one or more biomarkers that is/are indicative of a particularcompound, biological process, and/or type of microbe or cell. Thus, anindirect biomarker is spectrometric data generated from one or moremolecules that provides information about a different molecule. Forexample, a molecular fingerprint, such as, a lipid fingerprint, may beindicative of the expression of a particular protein, e.g., a receptor;or of a particular cell type or microbial type.

A lipid biomarker may optionally be selected from, e.g., fatty acids,glycerolipids, sterol lipids, sphingolipids, prenol lipids,saccharolipids and/or phospholipids. A brief overview of various lipidsis provided below, but it must be appreciated that any particular lipidmay fall into more than one of the groups mentioned herein.

A fatty acid is an aliphatic monocarboxylic acid. The fatty acid mayoptionally have a carbon chain comprising precisely or at least 4, 6, 8,10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 36, 38 or 40 carbons. Itmay optionally be monounsaturated, polyunsaturated, or saturated. It mayoptionally be an eicosanoid. It may, for example, be oleic acid,palmitic acid, arachidonic acid, a prostaglandin, a prostacyclin, athromboxane, a leukotriene, or an epoxyeicosatrienoic acid.

The glycerolipid may optionally be selected from e.g., monoacylglycerol,diacylglycerol, and/or triacylglycerol.

The sterol may optionally be selected from free sterols, acylatedsterols (sterol esters), alkylated sterols (stearyl alkyl ethers),sulfated sterols (sterol sulfate), sterols linked to a glycoside moiety(stearyl glycosides) and/or acylated sterols linked to a glycosidemoiety (acylated sterol glycosides).

The sterol may optionally have an aliphatic side chain of precisely orat least 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 10, 21, 22, 23,24, 25, 26, 27, 28, 29, 20, 35 or 40 carbon atoms. The number of carbonatoms in the aliphatic side chain may be expressed by the letter Cfollowed by the number, e.g., C27 for cholesterol. It may, for example,be selected from cholesterol, cholesterol sulphate, ergosterol,lanosterol, dinosterol (4a,23,24-trimethyl-5a-cholest-22E-en-3b-ol),oxysterol and/or a derivative of any thereof.

A phospholipid may comprise two fatty acids, a glycerol unit, aphosphate group and a polar molecule. The Phospholipid may optionallycomprise an ester, ether and/or other O-derivative of glycerol. Thephospholipid may optionally be selected from, e.g.,Phosphatidylglycerol, diphosphatidylglycerol (cardiolipin),Acylphosphatidylglycerol(1,2-diacyl-sn-glycero-3-phospho-(3′-acyl)-1′-sn-glycerol), and/orplasmalogen.

The phosphatidylglycerol lipid may optionally be selected fromphosphatidic acids (PAs), phosphatidylethanolamines (PEs),phosphatidylglycerols (PGs), phosphatidylcholines (PCs),phosphatidylinositols (PIs) and/or phosphatidylserines (PSs).

A sphingolipid is a lipid containing a sphingoid. It may optionally beselected from, e.g., a ceramide, i.e. an N-acylated sphingoid;sphingomyelin, i.e. a ceramide-1-phosphocholine; phosphoethanolaminedihidroceramide, and/or a glycosphingolipid, i.e. a lipid containing asphingoid and one or more sugars. For example, it may optionally be aglycosylated ceramide.

The biomarker may optionally be a metabolite, such as, a primary or asecondary metabolite; an antibiotic; a quorum sensing molecule; a fattyacid synthase product; a pheromone; and/or a biopolymer.

A biomarker compound may optionally be characterised by one or more ofthe following functional groups: alcohol, ester, alkane, alkene, alkyne,ether, ketone, aldehyde, anhydride, amine, amide, nitrile, aromatic,carboxylic acid, alkyl halide, and/or carbonyl. Optionally, it mayadditionally be identified as being primary, secondary or tertiary,e.g., a primary alcohol, a secondary amine, or the like.

For example, it may optionally be a terpene; prenylquinone; sterol;terpenoid; alkaloid; glycoside; surfactin; lichenysin,2-Heptyl-3-hydroxy-4(1H)-quinolone or 2-heptyl-3,4-dihydroxyquinoline(“PQS” or Pseudomonas quinolone signal); 4-hydroxy-2-heptylquinoline(“HHQ”); phenol, such as, a natural phenol; phenazine; biphenyl;dibenzofurans; beta-lactam; polyketide; rhamnolipid; mycolic acids;and/or polyhydroxyalkanoates;

The biomarker may optionally be selected from, e.g.,Glycerophosphocholines, Sphingomyelins, Glycerophospholipids,Galactoceramides, Glycerophosphoinositols, Glycerophosphoserines,Glycerophosphoglycerols, Cholesterol sulphate, sulfatides, seminolipids,citric acid, Glycerophosphoethanolamines, Glycerophosphoethanolamines,2-hydroxyglutarate, glutamine, glutamate, succinate, fumarate,palmitoylglycine, ubiquinones, gadoteridol and/or any of the otherbiomarkers mentioned herein, including any of the Tables.

The inventors have identified inter alia the following biomarkers:

Mycolic acids for bacteria belonging to the Corynebacterineae subordersuch as Mycobacterium spp., Corynebacterium spp. and Rhodococcus spp. Inparticular, the following mycolic acids have been detected from thecorresponding genera: Mycobacterium spp.: C77-C81 (even and oddnumbered, 0-2 unsaturations); Corynebacterium spp.: C28-C36 (evennumbered, 0-2 unsaturations); Nocardia spp.: C48-C56 (even numbered, 0-3unsaturations); Rhodococcus spp.: C28-C38 (even and odd numbered, 0-4unsaturations).

A variety of sphingolipid species were found to be specific for membersof the Bacteroidetes phylum. These sphingolipids include oxidizedceramides species, phosphoethanolamine dihydroceramides andC15:0-substituted phosphoglycerol dihydroceramides and dihydroceramide.Among those sphingolipid species, a series of galactosylatedsphingolipids was found to be specific for Bacteroides fragilis(Bacteroides fragilis alpha-Galactosylceramides).

Among bacteria, plasmalogens are highly specific for anaerobic bacteriasuch as Clostridium spp. and Fusobacterium spp. This is due to the factthat aerobic bacteria lost the biochemical pathway required forplasmalogen synthesis. Humans are able to synthesize plasmalogens(although via a different biochemical pathway from anaerobes), althoughthese were generally found to have longer chain lengths than bacterialplasmalogens.

Other biomarkers that are indicative of a certain group of bacteriainclude, for instance, lipopeptides that are produced specifically bycertain Bacillus species, such as, surfactin for B. subtilis andlichenysin for B. licheniformis. Production of these two molecules alsoenables straightforward differentiation of these otherwise very closelyrelated bacteria. A further example includes PQS-derived quorum-sensingmolecules and mono- and di-rhamnolipid species found for Pseudomonasaeruginosa.

Quorum sensing is a form of cell-to-cell communication which relies onthe principle that when a single microbe releases quorum sensingmolecules into the environment, the concentration of such molecules istoo low to be detected. However, when sufficient bacteria are present,quorum sensing molecule concentrations reach a threshold level thatallows the microbes to sense a critical cell mass and, in response, toactivate or repress particular genes. Quorum sensing molecules maytherefore also be referred to as autoinducers. Pathogens may use quorumsensing molecules as virulence factors.

Some examples of quorum sensing molecules are listed above. Additionalexamples include N-acyl homoserine lactones (N-acyle HSLs), such as,3-oxo-C₈-HSL, 3-oxo-C₁₀-HSL, or 3-oxo-C₁₂-HSL; diketopiperazines;3-hydroxypalmitic acid methyl ester; and peptide-based quorum sensingmolecules, such as, that of Staphylococcus aureus, which is anoligopeptide that has been termed the autoinducing peptide (AIP),encoded by the gene agrD. The active AIP is 7-9 amino acids, with a5-membered thiolactone ring.

By way of example, sphingomyelin lipids may optionally be a biomarker,e.g., for cancer; ergosterol may optionally be a biomarker, e.g., forfungi; dinosterol may optionally be a biomarker, e.g., fordinoflagellates; cholesterol sulphate may optionally be a biomarker,e.g., for cancer; 2-hydroxyglutarate may optionally be a biomarker,e.g., for cancer; and/or one or more sulfatides may optionally be abiomarker, e.g., for cancer, for example, astrocytoma. Optionally, thesulfatide may be selected from C₄₈H₉₁NO₁₁S, C₄₈H₉₂NO₁₂S, and/orC₅₀H₉₄NO₁₁S.

Iso-C15:0-substituted phosphoglycerol dihydroceramides may be specificfor the Porphyromonadaceae family. m/z=566.4790 may be a biomarker formembers of the Flavobacteria class.

Diseases

As mentioned elsewhere herein, the cell population may be diseased,e.g., it may originate from diseased tissue, and/or have beenmanipulated to be diseased.

The analysis may optionally relate to a disease or condition, such as,any of the diseases or conditions listed in this section and/orelsewhere herein. The terms “disease” and “condition” are usedinterchangeably herein.

The disease may optionally be a skin condition, which may optionally beselected, for example, from Acne, Alopecia, Boils, Bowen's Disease,Bullous pemphigoid (BP), Carbuncle, Cellulitis, Chilblains, Cysts,Darier's disease, Dermatitis, Dermatomyositis, Eczema, Erythema,Exanthema, Folliculitis, Frostbite, Herpes, Ichthyosis, Impetigo,Intertrigo, Keratosis, Lichen planus, Linear IgA disease, Melanoma,Moles, Onychomycosis, Papilloma, Petechiae, Prurigo, Psoriasis, Rosacea,Scabies, Scleroderma, Sebaceous Cyst, Shingles/Chickenpox,Telangiectasia, Urticaria (Hives), Warts and/or Xeroderma.

The disease may optionally be a liver condition, which may optionally beselected from, for example, hepatitis, fatty liver disease, alcoholichepatitis, liver sclerosis and/or cirrhosis.

The disease may optionally be a lung condition, which may optionally beselected from, for example, Asthma, Atelectasis, Bronchitis, Chronicobstructive pulmonary disease (COPD), Emphysema, Lung cancer, Pneumonia,Pulmonary edema, Pneumothorax, and/or Pulmonary embolus.

The thyroid gland is an endocrine gland which normally producesthyroxine (T4) and triiodothyronine (T3). The disease may optionally bea thyroid condition, which may optionally be, e.g., hypothyroidism orhyperthyroidism.

The disease may optionally be a cancer or tumour, which may optionallybe selected from, for example, carcinomas, sarcomas, leukaemias,lymphomas and gliomas.

More particularly, it may optionally be selected from, for example,Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML),Adrenocortical Carcinoma, adenoma, Anal Cancer, Appendix Cancer,Astrocytomas, Basal Cell Carcinoma, Bile Duct Cancer, Birch-Hirschfield,Blastoma, Bladder Cancer, Bone Cancer, Ewing Sarcoma, Osteosarcoma,Malignant Fibrous Histiocytoma, Brain Stem Glioma, Brain cancer,glioblastoma multiforme (“GBM”), Astrocytomas, Spinal Cord cancer,Craniopharyngioma, Breast Cancer, Bronchial Tumour, Burkitt Lymphoma,Carcinoid Tumour, Cervical Cancer, Cholangiocarcinoma, Chordoma, ChronicLymphocytic Leukemia (CLL), Chronic Myelogenous Leukemia (CML), ChronicMyeloproliferative Neoplasms, Colon Cancer, Colorectal Cancer,Craniopharyngioma, Childhood, Ductal Carcinoma In Situ (DCIS),Endometrial Cancer, Ependymoma, Esophageal Cancer,Esthesioneuroblastoma, Fibroadenoma, Intraocular Melanoma,Retinoblastoma, Fallopian Tube Cancer, Gallbladder Cancer, Gastric(Stomach) Cancer, Germinoma, Hairy Cell Leukemia, Head and Neck Cancer,Heart Cancer, Hepatocarcinoma, Hodgkin Lymphoma, Hypopharyngeal Cancer,Kahler, Kaposi Sarcoma, Kidney cancer, Laryngeal Cancer, Leiomyoma, Lipand Oral Cavity Cancer, Liver Cancer, Lung Cancer (such as, Non-SmallCell or Small Cell), Lymphoma, Lymphoblastoma, Male Breast Cancer,Malignant Fibrous Histiocytoma of Bone, Melanoma, Melanocarcinoma,Medulloblastoma, Merkel Cell Carcinoma, Mesothelioma, Mouth Cancer,Myeloma, Multiple Myeloma, Mycosis Fungoides, Myeloproliferativedisorder, Nasal Cavity and Paranasal Sinus Cancer, NasopharyngealCancer, Neuroblastoma, Nephroblastoma, Non-Hodgkin Lymphoma, OralCancer, Oropharyngeal Cancer, Osteosarcoma, Ovarian Cancer, PancreaticCancer, Papillomatosis, Paraganglioma, Parathyroid Cancer, PenileCancer, Peritoneal cancer, Pharyngeal Cancer, Pheochromocytoma,Pineoblastoma, Pituitary Tumour, Prostate Cancer, Rectal Cancer,Retinoblastoma, Rhabdomyosarcoma, Salivary Gland Cancer, SézarySyndrome, Skin Cancer, Seminoma, Teratoma, Testicular Cancer, ThroatCancer, Thyroid Cancer, thoracic cancer, Urethral Cancer, VaginalCancer, Vulvar Cancer, Waldenstrom macroglobulinemia, and/or Wilm'stumour. In the above list, any reference to a “cancer” or a “tumour”should be understood to include a reference to a “cancer and/or atumour” of that type.

Optionally, a brain cancer may be glioblastoma multiforme, glioblastoma,giant cell glioblastoma, recurrent gliobastoma, anaplastic astrocytoma,oligodendroglioma and/or diffuse astrocytoma.

If the cancer is breast cancer, it may optionally be selected from, forexample, ductal carcinoma in situ (DCIS), lobular carcinoma in situ(LCIS), Invasive breast cancer (NST), Invasive lobular breast cancer,Inflammatory breast cancer, breast cancer associated with Paget'sdisease and angiosarcoma of the breast.

The cancer may optionally be caused by, associated with, and/orcharacterised by a mutation or other genetic variation, which mayoptionally result in the altered expression of a molecule, e.g., amolecule comprising or consisting of a lipid, such as, a glycolipid orphospholipid; a carbohydrate; DNA; RNA; a protein; a polypeptide, suchas, a ribosomal peptide or a non-ribosomal peptide; an oligopeptide; alipoprotein; a lipopeptide; an amino acid; and/or a chemical compound,optionally an organic chemical compound. More particularly, a mutationmay optionally result in the altered expression of a protein and/ormetabolite.

A cancer may optionally express one or more metabolites that may serveas a biomarker for that cancer. For example, optionally a metabolitesuch as succinate, fumarate, 2-HG, and/or any of the other metabolitesmentioned herein may accumulate in a cancer.

Subtypes of cancer may optionally be identified, e.g., based on suchaltered expression. For example, a cancer may optionally be identifiedas being of a particular subtype based on the expression, or lackthereof, of a receptor, e.g., selected from estrogen receptors (ER),progesterone receptors (PR) and human epidermal growth factor receptor 2(HER2). A cancer may therefore, for example, be referred to as ERnegative if it lacks expression of ER; or be referred to astriple-negative breast cancer (TNBC), if it is ER−, PR− and Her2−.

The mutation may optionally, e.g., be in a gene encoding isocitratedehydrogenase 1 (IDH1) and/or 2 (IDH2) yielding mutant enzymes capableof converting alpha-ketoglutarate to 2-hydroxyglutarate (2-HG). Such amutation may optionally be present, e.g., in a glioma, intrahepaticcholangiocarcinoma, acute myelogenous leukaemia (AML) and/orchondrosarcomas. 2-HG may thus be referred to as an oncometabolite. 2-HGmay be present in very small amounts in normal tissues, whereas it maybe present in high concentrations, e.g., several micromoles per gram oftumour, in mutant tumours.

Thus, a cancer subtype may have a specific biomarker. The methodprovided herein may optionally involve the analysis of a cancer subtype.

The method may optionally involve the analysis of the phenotype and/orgenotype of a cancer, which may optionally involve an analysis of any ofthe mutations discussed above.

The disease may optionally be necrosis, which may optionally be causedby, or associated with, for example, injury, infection, cancer,infarction, toxins, inflammation, lack of proper care to a wound site,frostbite, diabetes, and/or arteriosclerosis. Optionally, the necrosismay be necrosis of cancerous or non-cancerous tissue. The necrosis mayoptionally, for example, be coagulative, liquefactive, caseous, fatnecrosis, fibrinoid necrosis and/or gangrenous necrosis.

The disease may optionally be selected from an autoimmune disorder, aninflammatory disease, tropical sprue, a food intolerance, an infection,a cancer, and/or any of the of the disorders mentioned herein.

More particularly, the disease may optionally be selected from, forexample, asthma, Coeliac disease, gastritis, peptic duodenitis,Gluten-sensitive enteropathy; allergy and/or intolerance to an allergen,e.g., to milk, soy, tree nut(s), egg, wheat, meat, fish, shellfish,peanut, seed, such as sesame, sunflower, and/or poppy seeds, garlic,mustard, coriander, and/or onion; Hashimoto's thyroiditis; Irritablebowel syndrome; Graves's disease; reactive arthritis; psoriasis;multiple sclerosis; Systemic lupus erythematosus (SLE or lupus);ankylosing spondylitis; progressive systemic sclerosis (PSS);glomerulonephritis; autoimmune enteropathy; IgA deficiency; commonvariable immunodeficiency; Crohn's disease; colitis, such as,lymphocytic colitis, collagenous colitis and/or ulcerative colitis;diffuse lymphocytic gastroenteritis; ulcer; intestinal T-cell lymphoma;infection, e.g., pharyngitis, bronchitis, and/or infection with amicrobe selected, for example, from Giardia, Cryptosporidium,Helicobacter and/or any of the other microbes mentioned herein.

The method may optionally allow an analysis of metabolic differencesbetween various conditions.

Analysis and/or Treatment of Disease

The method provided herein may optionally be used to study disease. Forexample, the cell population may serve as an in vitro or ex vivo modelof a disease. Optionally, the cell population may have been derived froma subject and/or be xenograft-derived.

Thus, optionally, a cell population having a particular disease may beused to monitor the progress of that disease at the cellular level.Optionally, the cell population may be manipulated, mutated, and/orexposed to a substance and/or environmental condition as discussedelsewhere herein and the effect thereof may be analysed to determine theeffect of such a manipulation, mutation, substance and/or environmentalcondition on a disease.

The method may optionally be used to monitor the progress of disease.The method may optionally be used to assess the effectiveness of atherapeutic or test substance.

Optionally, serial (periodic) analysis of a target for a change may beused to assess whether or not a therapeutic or test substance has beeneffective; the extent to which a therapeutic or test substance has beeneffective; whether or not a disease is re-occurring or progressing inthe cell population; and/or to assess the likely clinical outcome(prognosis) of the disease for a subject.

Any reference in this context to a “subject” is intended to be a subjecthaving the disease for which the cell population is a model.

Optionally, any of the methods provided herein may also include a stepof determining whether a subject should receive a treatment with atherapeutic substance, e.g., the substance tested on the cellpopulation.

Suitable treatments or substances are discussed elsewhere herein.Particularly, if the method involves a determination that a disease islikely to respond to treatment, and/or that a diseased cell populationhas responded to treatment, then the method may include a step ofdetermining that a subject should receive an appropriate treatment.

Optionally, any of the methods provided herein may also include a stepof determining, for a subject who is receiving, or has received,treatment, whether the treatment should be altered or ceased, based onthe treatment response determined for the model cell population. Forexample, the method may optionally include a step of determining thatthe treatment dose and/or frequency should be increased or decreased. Inparticular, if the method involves a determination that one or morebiomarkers for a disease are increased in the model cell population,have increased over time, or have not decreased (or not decreasedsufficiently) in response to a treatment, then the method may optionallyinclude a step of determining that the treatment dose and/or frequencyshould be increased; and if the method involves a determination that oneor more biomarkers for a disease are not increased in the model cellpopulation, have decreased over time, or have decreased in response to atreatment, then the method may optionally include a step of determiningthat the treatment dose and/or frequency should be decreased or that thetreatment may be ceased; or vice versa.

The method may include a step of determining that a particular treatmentshould be replaced by another treatment, for example that one drugshould be replaced with another drug. In particular, if the methodinvolves a determination that one or more biomarkers for a disease areincreased in the model cell population, have increased over time, orhave not decreased (or not decreased sufficiently) in response to atreatment, then the method may include a step of determining that thetreatment should be replaced by another treatment; and if the methodinvolves a determination that one or more biomarkers for a disease arenot increased in the model cell population, have decreased over time, orhave decreased in response to a treatment, then the method may include astep of determining that the treatment should not be replaced by anothertreatment; or, vice versa.

Optionally, any of the methods provided herein may also include a stepof administering a treatment to said subject. The method may then, forexample, be referred to as a method of diagnosis and treatment;monitoring and treatment; prognosis and treatment; prediction andtreatment; or stratification and treatment.

Optionally, any of the methods provided herein may be used inconjunction with any other known methods, particularly a knowndiagnostic, prognostic, predictive, and/or monitoring method for adisease.

Cell Population Infection

As mentioned elsewhere, a potential infection of a cell population maybe analysed. The infection may be, e.g., by another cell type and/or bya microbe.

A “microbe”, also known as a micro-organism, is an organism which is toosmall to be visible to the naked eye, i.e. is microscopic. A microbe maybe selected from bacteria, fungi, archaea, algae, protozoa and viruses.Although the terms bacteria, fungi, archaea, algae, protozoa and virusestechnically denote the plural form, it is common practice to use themalso to denote the singular form. Consequently, the terms “bacteria” and“bacterium” are used interchangeably herein; the terms “fungi” and“fungus” are used interchangeably herein; the terms “archaea” and“archaeum” are used interchangeably herein; the terms “protozoa” and“protozoum” are used interchangeably herein; and the terms “viruses” and“virus” are used interchangeably herein.

In the case of a microbe, analysis may optionally be on any taxonomiclevel, for example, at the Kingdom, Phylum or Division, Class, Order,Family, Genus, Species and/or Strain level.

“Taxonomy” is the classification of organisms, and each level ofclassification may be referred to as a “taxon” (plural: taxa). Organismsmay be classified into the following taxa in increasing order ofspecificity: Kingdom, Phylum or Division, Class, Order, Family, Genus,Species and Strain. Further subdivisions of each taxon may exist. Itmust be appreciated that within the vast scientific community there aresome discrepancies within some taxonomic classifications. There may alsobe a lack of consensus with regard to the nomenclature of certainmicrobes, resulting in a particular microbe having more than one name orin two different microbes having the same name.

As a shorthand, the term “type” of microbe is used to refer to a microbethat differs from another microbe at any taxonomic level.

In some embodiments, the microbe may be selected from bacteria, fungi,archaea, algae and protozoa. In some embodiments, it may be selectedfrom bacteria and fungi. In some embodiments, it may be selected frombacteria.

The microbe may be single-cellular or multi-cellular. If the microbe isa fungus, it may optionally be filamentous or single-cellular, e.g., ayeast.

A fungus may optionally be yeast. It may optionally be selected from thegenus Aspergillus, Arthroascus, Brettanomyces Candida, Cryptococcus,Debaryomyces, Geotrichum, Pichia, Rhodotorula, Saccharomyces,Trichosporon, and Zygotorulaspora.

It may optionally be selected from the species Arthroascus schoenii,Brettanomyces bruxellensis, Candida albicans, C. ascalaphidarum, C.amphixiae, C. antarctica, C. argentea, C. atlantica, C. atmosphaerica,C. blattae, C. bromeliacearum, C. carpophila, C. carvajalis, C.cerambycidarum, C. chauliodes, C. corydali, C. dosseyi, C. dubliniensis,C. ergatensis, C. fructus, C. glabrata, C. fermentati, C.guilliermondii, C. haemulonii, C. insectamens, C. insectorum, C.intermedia, C. jeffresii, C. kefyr, C. keroseneae, C. krusei, C.lusitaniae, C. lyxosophila, C. maltosa, C. marina, C. membranifaciens,C. milleri, C. mogii, C. oleophila, C. oregonensis, C. parapsilosis, C.quercitrusa, C. rugosa, C. sake, C. shehatea, C. temnochilae, C. tenuis,C. theae, C. tolerans, C. tropicalis, C. tsuchiyae, C. sinolaborantium,C. sojae, C. subhashii, C. viswanathii, C. utilis, C. ubatubensis, C.zemplinina, Cryptococcus neoformans, Cryptococcus uniguttulatus,Debaryomyces carsonii, Geotrichum capitatum, Trichosporon asahii,Trichosporon mucoides, Trichosporon inkin, Saccharomyces cerevisiae,Pichia acaciae, Pichia anomala, Pichia capsulata, Pichia farinosa,Pichia guilliermondii, Pichia spartinae, Pichia ohmeri, Rhodotorulaglutinous, Rhodotorula mucilaginosa, Saccharomyces boulardii,Saccharomyces cerevisiae, and/or Zygotorulaspora florentinus.

The bacteria may optionally be of a genus selected from, e.g.,Abiotrophia, Achromobacter, Acidovorax, Acinetobacter, Actinobacillus,Actinomadura, Actinomyces, Aerococcus, Aeromonas, Anaerococcus,Anaplasma, Bacillus, Bacteroides, Bartonella, Bifidobacterium,Bordetella, Borrelia, Brevundimonas, Brucella, BurkholderiaCampylobacter, Capnocytophaga, Chlamydia, Citrobacter, Chlamydophila,Chryseobacterium, Clostridium, Comamonas, Corynebacterium, Coxiella,Cupriavidus, Delftia, Dermabacter, Ehrlichia, Eikenella, Enterobacter,Enterococcus, Escherichia, Erysipelothrix, Facklamia, Finegoldia,Francisella, Fusobacterium, Gemella, Gordonia, Haemophilus,Helicobacter, Klebsiella, Lactobacillus, Legionella, Leptospira,Listeria, Micrococcus, Moraxella, Morganella, Mycobacterium, Mycoplasma,Neisseria, Nocardia, Orientia, Pandoraea, Pasteurella, Peptoniphilus,Peptostreptococcus, Plesiomonas, Porphyromonas, Pseudomonas, Prevotella,Proteus, Propionibacterium, Rhodococcus, Ralstonia, Raoultella,Rickettsia, Rothia, Salmonella, Serratia, Shigella, Staphylococcus,Stenotrophomonas, Streptococcus, Tannerella, Treponema, Ureaplasma,Vibrio or Yersinia.

The virus may optionally be a DNA virus, and RNA virus or a retrovirus.It may optionally be a single stranded (ss) or a double stranded (ds)virus. More particularly, it may optionally be a ssDNA, dsDNA, dsRNA,ssRNA (positive strand), ssRNA (negative strand), ssRNA (reversetranscribed) or dsDNA (reverse transcribed) virus.

It may optionally be selected from one or more of the Herpesviridae; theAdenoviridae; Papillomaviridae; Polyomaviridae; Poxviridae;Anelloviridae; Mycodnaviridae; Parvoviridae; Reoviridae; Coronaviridae;Astroviridae; Caliciviridae; Flaviviridae; Picornaviridae; Togaviridae;Rhabdoviridae; Filoviridae; Paramyxoviridae; Arenaviridae; Bunyaviridae;Orthomyxoviridae; Retroviridae; Epadnaviridae; Hepevirus; and/orDeltavirus.

Confocal Microscopy

The principle of confocal imaging aims to overcome some limitations oftraditional wide-field fluorescence microscopes. In a conventional(i.e., wide-field) fluorescence microscope, the entire specimen isflooded evenly in light from a light source. All parts of the specimenin the optical path are excited at the same time and the resultingfluorescence is detected by the microscope's photodetector or cameraincluding a large unfocused background part.

In contrast, a confocal microscope uses point illumination and a pinholein an optically conjugate plane in front of the detector to eliminateout-of-focus signal (the name “confocal” stems from this configuration).As only light produced by fluorescence very close to the focal plane canbe detected, the image's optical resolution, particularly in the sampledepth direction, is much better than that of wide-field microscopes.However, as much of the light from sample fluorescence is blocked at thepinhole, this increased resolution is at the cost of decreased signalintensity. As a result, relatively long exposures may be required.

As only one point in the sample is illuminated at a time, 2D or 3Dimaging requires scanning over a regular raster (i.e., a rectangularpattern of parallel scanning lines) in the specimen. The achievablethickness of the focal plane is defined mostly by the wavelength of theused light divided by the numerical aperture of the objective lens, butalso by the optical properties of the specimen. The thin opticalsectioning possible makes these types of microscopes particularly goodat 3D imaging and surface profiling of samples.

Optionally, cell imaging may be performed using a confocal microscope.

Flow Cytometry

Optionally, the method may additionally include a step of flowcytometry, e.g., prior to and/or after the mass and/or ion mobilityspectrometric analysis. For example, the method may optionally becarried out on a cell population that was previously analysed via flowcytometry, e.g., it may optionally be carried out on a sub-population ofcells sorted via fluorescence-assisted cell sorting (“FACS”).

Optionally, the method may comprise separating labelled cells fromunlabelled cells prior into a first and a second subset, optionally alabelled and an unlabelled subset. This separation may optionally bebefore and/or after the generation of spectrometric data. Thus,optionally, the target may be a subset of a cell population, wherein thesubset has been generated using flow cytometry, e.g., FACS.

Optionally, said steps of separating labelled cells from unlabelledcells may be carried out prior to performing the method provided herein.Optionally, (i) at least one of said subsets may be analysed directlyvia the method of any preceding claim; (ii) at least one subset may beintroduced directly into a mass spectrometer and/or ion mobilityspectrometer; and/or (iii) a FACS device may be coupled, optionallydirectly, to a device, optionally as defined elsewhere herein, e.g., amass spectrometer and/or ion mobility spectrometer.

In biotechnology, flow cytometry is a laser-based biophysical technologyemployed, e.g., in cell counting, cell sorting, biomarker detection andprotein engineering. Flow cytometry may optionally be used, e.g., foranalysing the expression of cell surface and/or intracellular molecules,characterizing and/or identifying different cell types in aheterogeneous cell population, assessing the purity of isolatedsubpopulations, and/or analysing cell size and volume. It allowssimultaneous multi-parameter analysis of single cells.

It may particularly be used to measure fluorescence intensity producedby ligands that bind to specific cell-associated molecules, e.g., (i)fluorescent-labelled antibodies detecting proteins; or (ii) propidiumiodide binding to DNA.

The staining procedure may involve making a single-cell suspension fromcell culture or tissue samples. The cells may then incubated, e.g., intubes and/or microtiter plates, with unlabelled or fluorochrome-labelledantibodies. Cells may be suspended in a stream of fluid and passed by anelectronic detection apparatus.

Flow cytometers are able to analyse several thousands or particles persecond. A flow cytometer comprises a flow cell in which a liquid streamcarries and aligns cells so that they pass single file through a lightbeam for sensing. The impedance or conductivity of the cells and variousoptical properties of the cells may be measured.

Flow cytometry is routinely used in the diagnosis of health disorders,especially blood cancers, but has many other applications in basicresearch, clinical practice and clinical trials. A common variation isto physically sort particles based on their properties, so as to purifypopulations of interest, e.g., by fluorescence-assisted cell sorting(“FACS”).

Fluorescence-activated cell sorting (FACS) is a method of sorting aheterogeneous mixture of biological cells into two or more containers,one cell at a time, based upon the specific light scattering andfluorescent characteristics of each cell. Thus, FACS allows the physicalsorting of a heterogeneous mixture of cells into 2 or more differentsub-populations.

As mentioned above, cells may be labelled with fluorescent labels thatare specific for a particular cellular marker. If a cell population isheterogeneous for that marker only the marker-positive subpopulation ofthe cells will become labelled.

A FACS apparatus may then be used to sort the cells. The cell suspensionis entrained in the centre of a narrow, rapidly flowing stream ofliquid. The flow is arranged so that there is a large separation betweencells relative to their diameter. A vibrating mechanism causes thestream of cells to break into individual droplets. The system isadjusted so that there is a low probability of more than one cell perdroplet. Just before the stream breaks into droplets, the flow passesthrough a fluorescence measuring station where the fluorescent characterof interest of each cell is measured. An electrical charging ring isplaced just at the point where the stream breaks into droplets. A chargeis placed on the ring based on the immediately prior fluorescenceintensity measurement, and the opposite charge is trapped on the dropletas it breaks from the stream. The charged droplets then fall through anelectrostatic deflection system that diverts droplets into containersbased upon their charge. In some systems, the charge is applied directlyto the stream, and the droplet breaking off retains charge of the samesign as the stream. The stream is then returned to neutral after thedroplet breaks off.

Fluorescent Labelling

Optionally, a molecule on or within one or more of the cells may belabelled. The label may, e.g., be a fluorescent label.

The following table details a list of fluorochromes forimmunofluorescence microscopy:

Fluorochromes excitation (nm) emission (nm) AMCA 347 445 Alexa Fluor 350345 440 Alexa Fluor 488 488 520 Cy2 492 510 FITC 496 518 Bodipy-FL 503511 TRITC 544 572 Cy3 550 570 LRSC 572 590 Rhodamine Red-X 570 590 TexasRed 596 620 Cy5 650 670 Alexa Fluor 647 650 668

wherein AMCA is aminomethylcoumarin acetic acid, Cy2 is cyanine, FITC isfluorescein isothiocyanate, TRITC is tetramethylrhodamineisothiocyanate, Cy3 is indocarbocyanine, LRSC is lissamine rhodaminesulfonyl chloride and Cy5 is indodicarbocyanine.

Green fluorescent protein (“GFP”) is a protein composed of 238 aminoacid residues (26.9 kDa) that exhibits bright green fluorescence whenexposed to light in the blue to ultraviolet range. Although many othermarine organisms have similar green fluorescent proteins, GFPtraditionally refers to the protein first isolated from the jellyfishAequorea victoria. The GFP from A. victoria has a major excitation peakat a wavelength of 395 nm and a minor one at 475 nm. Its emission peakis at 509 nm, which is in the lower green portion of the visiblespectrum. The fluorescence quantum yield (QY) of GFP is 0.79. The GFPfrom the sea pansy (Renilla reniformis) has a single major excitationpeak at 498 nm.

In cell and molecular biology, the GFP gene is frequently used as areporter of expression. In modified forms it has been used to makebiosensors, and many animals have been created that express GFP as aproof-of-concept that a gene can be expressed throughout a givenorganism. The GFP gene can be introduced into organisms and maintainedin their genome through breeding, injection with a viral vector, or celltransformation. To date, the GFP gene has been introduced and expressedin many bacteria, yeast and other fungi, fish (such as zebrafish),plant, fly and mammalian cells, including human.

Analysis of Spectrometric Data

Any of the methods of the invention may optionally involve the analysisof spectrometric data; more particularly, the analysis of spectrometricdata from a target, e.g., a first target location.

The analysis of a target may be based solely on the analysis ofspectrometric data, or it may optionally involve one or more furtheranalytical tools, details of which are discussed elsewhere herein.

In some embodiments, the spectrometric data may optionally providedirect information about the target or target entity.

For example, if a particular cell type has a specific spectrometricsignal pattern, then obtaining this signal pattern from a targetprovides direct information about the presence, identity and/orcharacteristics of that cell type.

For example, if a particular microbe has a specific spectrometric signalpattern, then obtaining this signal pattern from a target providesdirect information about the presence, identity and/or characteristicsof that microbe.

For example, if a particular compound has a specific spectrometricsignal pattern, then obtaining this signal pattern from a targetprovides direct information about the presence, identity and/orcharacteristics of that compound. This may be the case, for example, fora compound which is secreted by a cell and/or by a microbe, or for anagent, such as, a drug or a metabolite thereof.

However, in other embodiments, spectrometric data may optionally provideindirect information about the target or target entity. This may be thecase, for example, for a compound which is produced, but not secreted,by a cell and/or by a microbe. The presence of this compound mayoptionally be detected indirectly by detecting a spectrometric signalpattern which is characteristic of a cell and/or microbe containing saidcompound.

Spectrometric data obtained from a target, e.g., a first targetlocation, may optionally be compared to one or more other spectrometricdata, which may conveniently be referred to herein as “reference”,“control” or “comparator” spectrometric data. As explained elsewhereherein, analysing spectrometric data may optionally comprise analysingone or more sample spectra so as to classify an aerosol, smoke or vapoursample. This may comprise developing a classification model or libraryusing one or more reference sample spectra, or may comprise using anexisting library.

Optionally, an analysis may be made to determine whether spectrometricdata obtained from a target matches or corresponds sufficiently to the“reference”, “control” or “comparator” spectrometric data to make apositive determination.

The term “reference” spectrometric data is used herein to meanspectrometric data from a known cell type, microbe or compound.Reference spectrometric data may optionally be publicly available, orthe skilled person may generate a library of reference spectrometricdata. The method may optionally involve comparing the spectrometric datato one or more reference spectrometric data. If the spectrometric dataobtained from a target matches or corresponds sufficiently to areference spectrometric data, then optionally a positive determinationmay be made. Optionally, a positive determination may be made if thespectrometric data corresponds more closely to one library entry thanany other library entry. If the spectrometric data obtained from atarget does not match or correspond sufficiently to a referencespectrometric data, then optionally a negative determination may bemade.

The term “comparator” spectrometric data is used herein to meanspectrometric data obtained from a second target. The first and secondtargets may be different cell populations, or 2 separate samplesobtained from the same cell population. The method may optionallyinvolve comparing the spectrometric data to one or more comparatorspectrometric data. If the spectrometric data obtained from a targetmatches or corresponds sufficiently to a comparator spectrometric data,then optionally a positive determination may be made. If thespectrometric data obtained from a target does not match or correspondsufficiently to a comparator spectrometric data, then optionally anegative determination may be made.

The term “control” spectrometric data is used herein to meanspectrometric data obtained from the first target at an earlier point intime. Control spectrometric data may, for example, be used whenmonitoring, e.g., the growth and/or substance production by a cellculture. Any of the methods may optionally involve comparing thespectrometric data to one or more control spectrometric data. If thespectrometric data obtained from a target matches or correspondssufficiently to a control spectrometric data, then optionally a positivedetermination may be made. If the spectrometric data obtained from atarget does not match or correspond sufficiently to a controlspectrometric data, then optionally a negative determination may bemade.

By a “positive determination” is meant that the presence, identityand/or characteristics of a particular cell type, microbe and/orcompound is determined. For example, a positive determination mayinvolve determining that a target entity of a particular classificationis present; that a target entity has a certain characteristic; and/orthat a particular compound is present.

For example, in the case of a microbial target entity, a positivedetermination may, e.g., involve determining that a microbe of aparticular taxonomic rank is present; that a particular microbe has acertain characteristic, such as, resistance to a particular drug; and/orthat a particular compound is being produced by a microbe.

For example, in the case of a cell target entity, a positivedetermination may, e.g., involve determining that a cell has aparticular identity; and/or that a cell has a certain characteristic,such as, that it expresses a particular biomarker.

For example, in the case of a compound target entity, a positivedetermination may, e.g., involve determining that a particular type ofcompound is present; and/or that a compound has a certaincharacteristic, such as, a particular glycosylation pattern.

Thus, for example, if the spectrometric data of a first sample matchesor corresponds sufficiently to a reference spectrometric data, then thepresence in the first sample of a target entity corresponding to theentity from which the reference spectrometric data was obtained mayoptionally be confirmed. If the spectrometric data of a first samplematches or corresponds sufficiently to a reference spectrometric data,then the target entity present in the first sample may optionally beidentified as corresponding to the identity of the entity from which thereference spectrometric data was obtained. If the spectrometric data ofa first sample matches or corresponds sufficiently to a referencespectrometric data, then the target entity present in the first samplemay optionally be characterised as having a characteristic correspondingto the characteristic of the entity from which the referencespectrometric data was obtained. If the spectrometric data of a firstsample matches or corresponds sufficiently to a reference spectrometricdata, then a determination may optionally be made that the target entitypresent in the first sample produces the compound produced by the entityfrom which the reference spectrometric data was obtained.

As explained elsewhere herein, by determining or confirming the“identity” of a microbe or cell is meant that at least some informationabout the identity is obtained, which may, for example, be at anytaxonomic level. Thus, for example, if the reference spectrometric datais from Mycoplasma, then in one embodiment a match or sufficientcorrespondence may optionally be used to identify the first microbe asbelonging to the genus Mycoplasma, whereas in another embodiment a matchor sufficient correspondence may optionally be used to identify thefirst microbe as belonging to the species Mycoplasma genitalium.

As another example, if the spectrometric data of a first sample matchesor corresponds sufficiently to a comparator spectrometric data, then thepresence in the first sample of a target entity corresponding to theentity from which the comparator spectrometric data was obtained mayoptionally be confirmed. If the spectrometric data of a first samplematches or corresponds sufficiently to a comparator spectrometric data,then the target entity present in the first sample may optionally beidentified as corresponding to the identity of the entity from which thecomparator spectrometric data was obtained. If the spectrometric data ofa first sample matches or corresponds sufficiently to a comparatorspectrometric data, then the target entity present in the first samplemay optionally be characterised as having a characteristic correspondingto the characteristic of the entity from which the comparatorspectrometric data was obtained. If the spectrometric data of a firstsample matches or corresponds sufficiently to a comparator spectrometricdata, then a determination may optionally be made that the target entitypresent in the first sample produces the compound produced by the entityfrom which the comparator spectrometric data was obtained.

In other words, a match or sufficient correspondence to a reference orcomparator spectrometric data respectively may be used to confirm thatthe first target entity and the reference or comparator entityrespectively have the same identity, whereas the lack of a match orsufficient correspondence to a reference or comparator spectrometricdata respectively may be used to confirm that the first target entityand the reference or comparator entity respectively do not have the sameidentity.

By a “negative determination” is meant that the absence of a particulartarget entity is determined; and/or that it is determined that a targetentity does not have a particular identity and/or characteristic.

For example, a negative determination may involve determining that aparticular target entity is not present; that a particular target entitydoes not have a certain characteristic; and/or that a particularcompound is not present.

For example, in the case of a microbial target entity, a negativedetermination may, e.g., involve determining that a microbe of aparticular taxonomic rank is not present; that a particular microbe doesnot have a certain characteristic such as resistance to a particulardrug; and/or that a particular compound is not being produced.

For example, in the case of a cell target entity, a negativedetermination may, e.g., involve determining that a particular celltype, e.g., a HeLa cell, is not present; and/or that a cell does nothave a certain characteristic, such as, that it does not express aparticular cancer marker.

For example, in the case of a compound target entity, a negativedetermination may, e.g., involve determining that a particular type ofcompound is not present; and/or that a compound does not have a certaincharacteristic, such as, a particular glycosylation pattern.

Thus, for example, if the spectrometric data of a first sample does notmatch or correspond sufficiently to a reference spectrometric data, thenthe absence or insufficient presence in the first sample of a targetentity corresponding to the entity from which the referencespectrometric data was obtained may optionally be confirmed. If thespectrometric data of a first sample does not match or correspondsufficiently to a reference spectrometric data, then the target entitypresent in the first sample may optionally be identified as notcorresponding to the identity of the entity from which the referencespectrometric data was obtained. If the spectrometric data of a firstsample does not match or correspond sufficiently to a referencespectrometric data, then the target entity present in the first samplemay optionally be characterised as not having a characteristiccorresponding to the characteristic of the entity from which thereference spectrometric data was obtained. If the spectrometric data ofa first sample does not match or correspond sufficiently to a referencespectrometric data, then a determination may optionally be made that thetarget entity present in the first sample does not produce, orinsufficiently produces, the compound produced by the entity from whichthe reference spectrometric data was obtained.

As another example, if the spectrometric data of a first sample matchesor corresponds sufficiently to a control spectrometric data, then adetermination may be made that no, or no significant, change has takenplace, whereas if the spectrometric data of a first sample does notmatch or correspond sufficiently to a control spectrometric data, then adetermination may be made that a change, optionally a significantchange, has taken place. Examples of a change may, for example, be thepresence of a contaminating or infiltrating cell, microbe and/orcompound; or a change in the cell or microbe's behaviour or itsenvironment, such as, a change in the cell or microbe's growth rate,respiration rate; rate of production of a compound, such a secretedcompound; environmental temperature, pH, nutrient availability and soon.

As mentioned elsewhere herein, the method may optionally involve theanalysis of biomarkers.

If a biomarker for a target entity or disease status is known (e.g.,from the prior art or from the work disclosed herein), then the methodmay optionally involve analysing the target for the presence of thespectrometric signal of that biomarker. The spectrometric signal of anybiomarker may optionally be looked up in the literature, a database, or,if necessary, it may easily be determined experimentally.

For example, as determined herein, phosphatidylethanolamines such asPE(38:3) are a biomarker for fads2 gene expression, with a spectrometricsignal of m/z about 768.5578. When analysing a cell population target totry to distinguish between cells expressing the fads2 gene and cells notexpressing this gene, the method may optionally involve analysing thetarget for the presence of a spectrometric signal of m/z about 768.5578.

As mentioned elsewhere herein, the analyte giving rise to a particularspectrometric signal, e.g., a particular m/z, may optionally be furthercharacterised, e.g., using MS/MS. Thus, ionic species in the massspectra may optionally be identified based on exact mass measurements,e.g., with a mass deviation <3 ppm, and/or MS/MS fragmentation patterns.Isobaric lipids with different headgroups may optionally bedifferentiated by ion mobility.

Thus, optionally, the method may involve analysing the target for thepresence of a spectrometric signal of one or more biomarkers, optionallyselected from any of the biomarkers mentioned herein.

A biomarker for diseased cells may optionally be determined, e.g., bysubtracting the spectrometric signals obtained from normal cells fromthe spectrometric signals obtained from diseased cells, to arrive atspectrometric signals that are specific for the diseased cells.

The spectrometric data may comprise one or more sample spectra.Obtaining the spectrometric data may comprise obtaining the one or moresample spectra. Analysing the spectrometric data may comprise analysingthe one or more spectra. Obtaining the one or more sample spectra maycomprise a binning process to derive a set of time-intensity pairsand/or a set of sample intensity values for the one or more samplespectra. The binning process may comprise accumulating or histogrammingion detections and/or intensity values in a set of plural bins. Each binin the binning process may correspond to particular range of times ortime-based values, such as masses, mass to charge ratios, and/or ionmobilities. The bins in the binning process may each have a widthequivalent to a width in Da or Th (Da/e) in a range selected from agroup consisting of: (i) < or >0.01; (ii) 0.01-0.05; (iii) 0.05-0.25;(iv) 0.25-0.5; (v) 0.5-1.0; (vi) 1.0-2.5; (vii) 2.5-5.0; and (viii) <or >5.0. It has been identified that bins having widths equivalent towidths in the range 0.01-1 Da or Th (Da/e) can provide particularlyuseful sample spectra for classifying some aerosol, smoke or vapoursamples, such as samples obtained from tissues. The bins may or may notall have the same width. The widths of the bin in the binning processmay vary according to a bin width function. The bin width function mayvary with a time or time-based value, such as mass, mass to charge ratioand/or ion mobility. The bin width function may be non-linear (e.g.,logarithmic-based or power-based, such as square or square-root based).The bin width function may take into account the fact that the time offlight of an ion may not be directly proportional to its mass, mass tocharge ratio, and/or ion mobility. For example, the time of flight of anion may be directly proportional to the square-root of its mass and/ormass to charge ratio.

Spectrometric Library

The terms “spectrometric library” and “spectrometric database” are usedinterchangeably herein.

The skilled person may use any publicly available spectrometric data asreference spectrometric data. Examples of useful databases are:LipidMaps, LipidBlast and LipidXplorer, details of which are provided inthe following publications: “LipidBlast—in-silico tandem massspectrometry database for lipid identification” by Kind et al., NatMethods. 2013 August; 10(8): 755-758; “LipidXplorer: A Software forConsensual Cross-Platform Lipidomics” by Herzog et al. PLoS ONE 7(1):e29851; and “Lipid classification, structures and tools” by Fahy et al.Biochimica et Biophysica Acta (BBA)—Molecular and Cell Biology ofLipids, Volume 1811, Issue 11, November 2011, Pages 637-647, Lipidomicsand Imaging Mass Spectrometry, see also http://www.lipidmaps.org/.

Alternatively or in addition, the skilled person may construct aspectrometric library by obtaining spectrometric data from one or moresamples, which may optionally, in the case of microbes, include typeculture strains and/or clinical and/or environmental microbial isolates;in the case of cells or tissues, the sample(s) may optionally include acell line, cell culture, tissue sample and the like; in the case ofcompound, the sample(s) may optionally be purchased or synthesised.

Type culture strains and cell lines may optionally be obtained fromculture collections, such as, the American Type Culture Collection(ATCC) (10801 University Boulevard, Manassas, Va. 20110 USA).

The present inventors generated a spectrometric library using over 1500microbial strains, including clinical isolates and type culture strainsfrom the ATCC, encompassing about 95 genera and about 260 species ofbacteria and fungi. To expedite the generation of the spectrometriclibrary, the inventors set up high throughput culturing, automatedcolony imaging, colony picking and REIMS analysis.

The present inventors have also generated spectrometric libraries usingtissues and/or cell lines, details of which are provided elsewhereherein, including in the Examples.

The generation of a spectrometric library from microbes, cell linesand/or tissues may optionally be combined with a further analysis, e.g.,taxonomic classification and/or histology, e.g., based on any of thefurther analytical tools discussed elsewhere herein. For example, thetool may be DNA analysis. This may involve DNA sequencing, optionallypreceded by DNA isolation and/or amplification using, e.g., PCR. Forbacteria, sequencing of all or part of the 16S rRNA gene is particularlysuitable, whereas for fungi, sequencing of all or part of the internaltranscribed spacer (ITS) region is particularly suitable.

Analysing Sample Spectra

The step of analysing the spectrometric data may comprise analysing oneor more sample spectra so as to classify an aerosol, smoke or vapoursample.

Analysing the one or more sample spectra so as to classify the aerosol,smoke or vapour sample may comprise unsupervised analysis of the one ormore sample spectra (e.g., for dimensionality reduction) and/orsupervised analysis of the one or more sample spectra (e.g., forclassification).

Analysing the one or more sample spectra may comprise unsupervisedanalysis (e.g., for dimensionality reduction) followed by supervisedanalysis (e.g., for classification).

Analysing the one or more sample spectra may be performed as discussedelsewhere herein.

A list of analysis techniques which are intended to fall within thescope of the present invention are given in the following table:

Analysis Techniques Univariate Analysis Multivariate Analysis PrincipalComponent Analysis (PCA) Linear Discriminant Analysis (LDA) MaximumMargin Criteria (MMC) Library Based Analysis Soft Independent ModellingOf Class Analogy (SIMCA) Factor Analysis (FA) Recursive Partitioning(Decision Trees) Random Forests Independent Component Analysis (ICA)Partial Least Squares Discriminant Analysis (PLS-DA) Orthogonal (PartialLeast Squares) Projections To Latent Structures (OPLS) OPLS DiscriminantAnalysis (OPLS-DA) Support Vector Machines (SVM) (Artificial) NeuralNetworks Multilayer Perceptron Radial Basis Function (RBF) NetworksBayesian Analysis Cluster Analysis Kernelized Methods SubspaceDiscriminant Analysis K-Nearest Neighbours (KNN) Quadratic DiscriminantAnalysis (QDA) Probabilistic Principal Component Analysis (PPCA) Nonnegative matrix factorisation K-means factorisation Fuzzy c-meansfactorisation Discriminant Analysis (DA)

Combinations of the foregoing analysis approaches can also be used, suchas PCA-LDA, PCA-MMC, PLS-LDA, etc.

Analysing the sample spectra can comprise unsupervised analysis fordimensionality reduction followed by supervised analysis forclassification.

By way of example, a number of different analysis techniques will now bedescribed in more detail.

Multivariate Analysis—Developing a Model for Classification

By way of example, a method of building a classification model usingmultivariate analysis of plural reference sample spectra will now bedescribed.

FIG. 15 shows a method 1500 of building a classification model usingmultivariate analysis. In this example, the method comprises a step 1502of obtaining plural sets of intensity values for reference samplespectra. The method then comprises a step 1504 of unsupervised principalcomponent analysis (PCA) followed by a step 1506 of supervised lineardiscriminant analysis (LDA). This approach may be referred to herein asPCA-LDA. Other multivariate analysis approaches may be used, such asPCA-MMC. The PCA-LDA model is then output, for example to storage, instep 1508.

The multivariate analysis such as this can provide a classificationmodel that allows an aerosol, smoke or vapour sample to be classifiedusing one or more sample spectra obtained from the aerosol, smoke orvapour sample. The multivariate analysis will now be described in moredetail with reference to a simple example.

FIG. 16 shows a set of reference sample spectra obtained from twoclasses of known reference samples. The classes may be any one or moreof the classes of target described herein. However, for simplicity, inthis example the two classes will be referred as a left-hand class and aright-hand class.

Each of the reference sample spectra has been pre-processed in order toderive a set of three reference peak-intensity values for respectivemass to charge ratios in that reference sample spectrum. Although onlythree reference peak-intensity values are shown, it will be appreciatedthat many more reference peak-intensity values (e.g., ˜100 referencepeak-intensity values) may be derived for a corresponding number of massto charge ratios in each of the reference sample spectra. In otherembodiments, the reference peak-intensity values may correspond to:masses; mass to charge ratios; ion mobilities (drift times); and/oroperational parameters.

FIG. 17 shows a multivariate space having three dimensions defined byintensity axes. Each of the dimensions or intensity axes corresponds tothe peak-intensity at a particular mass to charge ratio. Again, it willbe appreciated that there may be many more dimensions or intensity axes(e.g., ˜100 dimensions or intensity axes) in the multivariate space. Themultivariate space comprises plural reference points, with eachreference point corresponding to a reference sample spectrum, i.e., thepeak-intensity values of each reference sample spectrum provide theco-ordinates for the reference points in the multivariate space.

The set of reference sample spectra may be represented by a referencematrix D having rows associated with respective reference samplespectra, columns associated with respective mass to charge ratios, andthe elements of the matrix being the peak-intensity values for therespective mass to charge ratios of the respective reference samplespectra.

In many cases, the large number of dimensions in the multivariate spaceand matrix D can make it difficult to group the reference sample spectrainto classes. PCA may accordingly be carried out on the matrix D inorder to calculate a PCA model that defines a PCA space having a reducednumber of one or more dimensions defined by principal component axes.The principal components may be selected to be those that comprise or“explain” the largest variance in the matrix D and that cumulativelyexplain a threshold amount of the variance in the matrix D.

FIG. 18 shows how the cumulative variance may increase as a function ofthe number n of principal components in the PCA model. The thresholdamount of the variance may be selected as desired.

The PCA model may be calculated from the matrix D using a non-lineariterative partial least squares (NIPALS) algorithm or singular valuedecomposition, the details of which are known to the skilled person andso will not be described herein in detail. Other methods of calculatingthe PCA model may be used.

The resultant PCA model may be defined by a PCA scores matrix S and aPCA loadings matrix L. The PCA may also produce an error matrix E, whichcontains the variance not explained by the PCA model. The relationshipbetween D, S, L and E may be:D=SL^(T)+E   (1)

FIG. 19 shows the resultant PCA space for the reference sample spectraof FIGS. 16 and 17 . In this example, the PCA model has two principalcomponents PC₀ and PC₁ and the PCA space therefore has two dimensionsdefined by two principal component axes. However, a lesser or greaternumber of principal components may be included in the PCA model asdesired. It is generally desired that the number of principal componentsis at least one less than the number of dimensions in the multivariatespace.

The PCA space comprises plural transformed reference points or PCAscores, with each transformed reference point or PCA score correspondingto a reference sample spectrum of FIG. 16 and therefore to a referencepoint of FIG. 17 .

As is shown in FIG. 19 , the reduced dimensionality of the PCA spacemakes it easier to group the reference sample spectra into the twoclasses. Any outliers may also be identified and removed from theclassification model at this stage.

Further supervised multivariate analysis, such as multi-class LDA ormaximum margin criteria (MMC), in the PCA space may then be performed soas to define classes and, optionally, further reduce the dimensionality.

As will be appreciated by the skilled person, multi-class LDA seeks tomaximise the ratio of the variance between classes to the variancewithin classes (i.e., so as to give the largest possible distancebetween the most compact classes possible). The details of LDA are knownto the skilled person and so will not be described herein in detail.

The resultant PCA-LDA model may be defined by a transformation matrix U,which may be derived from the PCA scores matrix S and class assignmentsfor each of the transformed spectra contained therein by solving ageneralised eigenvalue problem.

The transformation of the scores S from the original PCA space into thenew LDA space may then be given by:Z═SU   (2)

where the matrix Z contains the scores transformed into the LDA space.

FIG. 20 shows a PCA-LDA space having a single dimension or axis, whereinthe LDA is performed in the PCA space of FIG. 19 . As is shown in FIG.20 , the LDA space comprises plural further transformed reference pointsor PCA-LDA scores, with each further transformed reference pointcorresponding to a transformed reference point or PCA score of FIG. 19 .

In this example, the further reduced dimensionality of the PCA-LDA spacemakes it even easier to group the reference sample spectra into the twoclasses. Each class in the PCA-LDA model may be defined by itstransformed class average and covariance matrix or one or morehyperplanes (including points, lines, planes or higher orderhyperplanes) or hypersurfaces or Voronoi cells in the PCA-LDA space.

The PCA loadings matrix L, the LDA matrix U and transformed classaverages and covariance matrices or hyperplanes or hypersurfaces orVoronoi cells may be output to a database for later use in classifyingan aerosol, smoke or vapour sample.

The transformed covariance matrix in the LDA space V′_(g) for class gmay be given byV′_(g)=U^(T)V_(g)U   (3)

where V_(g) are the class covariance matrices in the PCA space.

The transformed class average position z_(g) for class g may be given bys _(g)U=z _(g)   (4)

where s_(g) is the class average position in the PCA space.

Multivariate Analysis—Using a Model for Classification

By way of example, a method of using a classification model to classifyan aerosol, smoke or vapour sample will now be described.

FIG. 21 shows a method 2100 of using a classification model. In thisexample, the method comprises a step 2102 of obtaining a set ofintensity values for a sample spectrum. The method then comprises a step2104 of projecting the set of intensity values for the sample spectruminto PCA-LDA model space. Other classification model spaces may be used,such as PCA-MMC. The sample spectrum is then classified at step 2106based on the project position and the classification is then output instep 2108.

Classification of an aerosol, smoke or vapour sample will now bedescribed in more detail with reference to the simple PCA-LDA modeldescribed above.

FIG. 22 shows a sample spectrum obtained from an unknown aerosol, smokeor vapour sample. The sample spectrum has been pre-processed in order toderive a set of three sample peak-intensity values for respective massto charge ratios. As mentioned above, although only three samplepeak-intensity values are shown, it will be appreciated that many moresample peak-intensity values (e.g., ˜100 sample peak-intensity values)may be derived at many more corresponding mass to charge ratios for thesample spectrum. Also, as mentioned above, in other embodiments, thesample peak-intensity values may correspond to: masses; mass to chargeratios; ion mobilities (drift times); and/or operational parameters.

The sample spectrum may be represented by a sample vector d_(x), withthe elements of the vector being the peak-intensity values for therespective mass to charge ratios. A transformed PCA vector s_(x) for thesample spectrum can be obtained as follows:d _(x)L=s _(x)   (5)

Then, a transformed PCA-LDA vector z_(x) for the sample spectrum can beobtained as follows:s _(x)U=z _(x)   (6)

FIG. 23 again shows the PCA-LDA space of FIG. 20 . However, the PCA-LDAspace of FIG. 23 further comprises the projected sample point,corresponding to the transformed PCA-LDA vector z, derived from the peakintensity values of the sample spectrum of FIG. 22 .

In this example, the projected sample point is to one side of ahyperplane between the classes that relates to the right-hand class, andso the aerosol, smoke or vapour sample may be classified as belonging tothe right-hand class.

Alternatively, the Mahalanobis distance from the class centres in theLDA space may be used, where the Mahalanobis distance of the point z_(x)from the centre of class g may be given by the square root of:(z _(x) −z _(g))^(T)(V′₉)(z _(x) −z _(g))   (8)and the data vector d_(x) may be assigned to the class for which thisdistance is smallest.

In addition, treating each class as a multivariate Gaussian, aprobability of membership of the data vector to each class may becalculated.

Library Based Analysis—Developing a Library for Classification

By way of example, a method of building a classification library usingplural input reference sample spectra will now be described.

FIG. 24 shows a method 2400 of building a classification library. Inthis example, the method comprises a step 2402 of obtaining plural inputreference sample spectra and a step 2404 of deriving metadata from theplural input reference sample spectra for each class of sample. Themethod then comprises a step 2406 of storing the metadata for each classof sample as a separate library entry. The classification library isthen output, for example to electronic storage, in step 2408.

A classification library such as this allows an aerosol, smoke or vapoursample to be classified using one or more sample spectra obtained fromthe aerosol, smoke or vapour sample. The library based analysis will nowbe described in more detail with reference to an example.

In this example, each entry in the classification library is createdfrom plural pre-processed reference sample spectra that arerepresentative of a class. In this example, the reference sample spectrafor a class are pre-processed according to the following procedure:First, a re-binning process is performed. In this embodiment, the dataare resampled onto a logarithmic grid with abscissae:

$x_{i} = \left\lfloor {N_{chan}\;\log{\frac{m}{M_{\min}}/\log}\;\frac{M_{\max}}{M_{\min}}} \right\rfloor$where N_(chan) is a selected value and [x] denotes the nearest integerbelow x. In one example, N_(chan) is 2¹² or 4096.

Then, a background subtraction process is performed. In this embodiment,a cubic spline with k knots is then constructed such that p % of thedata between each pair of knots lies below the curve. This curve is thensubtracted from the data. In one example, k is 32. In one example, p is5. A constant value corresponding to the q % quantile of the intensitysubtracted data is then subtracted from each intensity. Positive andnegative values are retained. In one example, q is 45.

Then, a normalisation process is performed. In this embodiment, the dataare normalised to have mean y _(i). In one example, y _(i)=1.

An entry in the library then consists of metadata in the form of amedian spectrum value μ_(i) and a deviation value D_(i) for each of theN_(chan) points in the spectrum.

The likelihood for the i'th channel is given by:

$\begin{matrix}{{{P{r\left( {\left. y_{i} \middle| \mu_{i} \right.,D_{i}} \right)}} = {\frac{1}{D_{i}}\frac{C^{C - {1/2}}{\Gamma(C)}}{\sqrt{\pi}{\Gamma\left( {C - {1/2}} \right)}}}}\frac{1}{\left( {C + \frac{\left( {y_{i} - \mu_{i}} \right)^{2}}{D_{i}^{2}}} \right)^{c}}} & \;\end{matrix}$

where ½ s C<∞ and where Γ(C) is the gamma function.

The above equation is a generalised Cauchy distribution which reduces toa standard Cauchy distribution for C=1 and becomes a Gaussian (normal)distribution as C→∞. The parameter D_(i) controls the width of thedistribution (in the Gaussian limit D_(i)=σ_(i) is simply the standarddeviation) while the global value C controls the size of the tails.

In one example, C is 3/2, which lies between Cauchy and Gaussian, sothat the likelihood becomes:

${P{r\left( {\left. y_{i} \middle| \mu_{i} \right.,D_{i}} \right)}} = {\frac{3}{4}\frac{1}{D_{i}}\frac{1}{\left( {{3/2} + {\left( {y_{i} - \mu_{i}} \right)^{2}/D_{i}^{2}}} \right)^{3/2}}}$

For each library entry, the parameters μ_(i) are set to the median ofthe list of values in the i'th channel of the input reference samplespectra while the deviation D_(i) is taken to be the interquartile rangeof these values divided by √2. This choice can ensure that thelikelihood for the i'th channel has the same interquartile range as theinput data, with the use of quantiles providing some protection againstoutlying data.

Library-Based Analysis—Using a Library for Classification

By way of example, a method of using a classification library toclassify an aerosol, smoke or vapour sample will now be described.

FIG. 25 shows a method 2500 of using a classification library. In thisexample, the method comprises a step 2502 of obtaining a set of pluralsample spectra. The method then comprises a step 2504 of calculating aprobability or classification score for the set of plural sample spectrafor each class of sample using metadata for the class entry in theclassification library. The sample spectra are then classified at step2506 and the classification is then output in step 2508.

Classification of an aerosol, smoke or vapour sample will now bedescribed in more detail with reference to the classification librarydescribed above.

In this example, an unknown sample spectrum y is the median spectrum ofa set of plural sample spectra. Taking the median spectrum y can protectagainst outlying data on a channel by channel basis.

The likelihood L_(s) for the input data given the library entry s isthen given by:

$L_{s} = {{P{r\left( {\left. y \middle| \mu \right.,D} \right)}} = {\prod\limits_{i = 1}^{N_{chan}}{P{r\left( {\left. y_{i} \middle| \mu_{i} \right.,D_{i}} \right)}}}}$

where μ_(i) and D_(i) are, respectively, the library median values anddeviation values for channel i. The likelihoods L_(s) may be calculatedas log likelihoods for numerical safety.

The likelihoods L_(s) are then normalised over all candidate classes ‘s’to give probabilities, assuming a uniform prior probability over theclasses. The resulting probability for the class s is given by:

${P{r\left( \overset{˜}{s} \middle| y \right)}} = \frac{L_{\overset{\sim}{s}}^{({1/F})}}{\sum_{s}L_{s}^{({1/F})}}$

The exponent (1/F) can soften the probabilities which may otherwise betoo definitive. In one example, F=100. These probabilities may beexpressed as percentages, e.g., in a user interface.

Alternatively, RMS classification scores R_(s) may be calculated usingthe same median sample values and derivation values from the library:

${R_{s}\left( {y,\mu,D} \right)} = \sqrt{\frac{1}{N_{chan}}{\sum\limits_{i = 1}^{N_{chan}}\;\frac{\left( {y_{i} - \mu_{i}} \right)^{2}}{D_{i}^{2}}}}$

Again, the scores R_(s) are normalised over all candidate classes ‘s’.

The aerosol, smoke or vapour sample may then be classified as belongingto the class having the highest probability and/or highest RMSclassification score.

Further Analytical Tools

Any of the methods of the invention may optionally include a step ofusing one or more additional analytical tools. Such a tool may, forexample, be selected from microscopic examination; nucleic acidanalysis, for example, using restriction enzymes, hybridisation,polymerase chain reaction (PCR) amplification and/or sequencing; and/ortesting for antigens. Such tools are well known in the art, but briefdetails are provided below.

The specimen may be examined visually, without any additional aids, suchas, a microscope.

Microscopic examination may, for example, optionally be light microscopyand/or electron microscopy.

Nucleic acid analysis may optionally involve isolation and purificationof DNA and/or RNA.

Nucleic acid analysis via PCR amplification may, for example, optionallyinvolve amplification of all or part of a suitable gene. For example, inthe case of a microbe, the gene may be the bacterial 16S rRNA gene, anduniversal and/or species-specific primers may be used. Other examples ofsuitable microbial genes which may optionally be analysed alternativelyor in addition include, for example, microbial species-specific genes orvirulence genes, for example, Shiga toxin (stx), intimin (eae),flagellar H-antigen genes fliC-fliA, hsp65, rpoB and/or recA. For fungi,PCR amplification of all or part of the internal transcribed spacer(ITS) is particularly suitable. When analysing human or animal cells,PCR may, e.g., be used to amplify a disease-specific and/or atissue-specific gene.

Optionally, the PCR may be Real-time PCR or quantitative PCR.Optionally, Reverse-transcriptase polymerase chain reaction (RT-PCR) maybe used to analyse RNA expression.

Nucleic acid analysis with restriction enzymes may, for example,optionally involve restriction-fragment length polymorphism (RFLP)analysis. RFLP, is a technique that exploits variations in the length ofhomologous DNA sequences. RFLP analysis may involve a restrictiondigest, i.e. incubating a DNA with a suitable restriction enzyme such asBamHI, HindIII or EcoRI. Each restriction enzyme can recognise and cut aspecific short nucleic acid sequence. The resulting DNA fragments maythen be separated by length, for example, through agarose gelelectrophoresis. The DNA fragments in the gel may optionally be stained,for example, with ethidium bromide, and the pattern of the fragments ofdifferent length may be determined.

Optionally, the DNA fragment may be transferred to a membrane via theSouthern blot procedure. The membrane may then be exposed to a labelledDNA probe to allow hybridisation to occur. The label may, for example,be or comprise a radioactive isotope or digoxigenin (DIG). Anyunhybridised probe may then be washed off. The label may then bedetected and the pattern of the fragments which have hybridised to thelabelled probe may be determined.

Sequencing may, for example, optionally involve the dideoxy or chaintermination method. In this method, the DNA may be used as a template togenerate a set of fragments that differ in length from each other by asingle base. The fragments may then be separated by size, and the basesat the end may be identified, recreating the original sequence of theDNA.

Hybridisation analysis may, for example, optionally include DNA-DNAhybridization of one or more selected DNA fragments, genes or wholegenomic DNA from a first cell or microbe to a labelled DNA probe todetermine the genetic similarity between the first cell or microbe andthe known or comparator cell or microbe. Hybridisation analysis may, forexample, involve transfer of the DNA to a membrane via the Southern blotprocedure, labelling and detection as described above.

Nucleic acid analysis may optionally involve e.g., denaturing gradientgel electrophoresis (DGGE) and/or temperature gradient gelelectrophoresis (TGGE).

Fatty acid profiling of cells or microbes may, for example, optionallybe carried out using gas-chromatography coupled to a flame ionisationdetector (GC-FID), or high performance liquid chromatography (HPLC).

With respect to microbial colony morphology, one or more of thefollowing may, for example, optionally be examined: size; whole colonyshape, which may, for example, be circular, irregular, or rhizoid;colony edge, which may, for example, be smooth, filamentous, orundulating; elevation, which may, for example, be flat, raised, convexor crateriform; surface, which may, for example, be wrinkled, rough,waxy, or glistening; opacity, which may, for example, be transparent,translucent, or opaque; pigmentation; colour, which may, for example, bered, yellow, or white; and/or water solubility.

With respect to the morphology of individual microbes, this may, forexample, optionally be determined to be a coccus (spherical), bacillus(rod-shaped), spiral (twisted), or pleomorphic. Cocci may optionally bea single coccus, diplococcic, streptococci, tetrads, sarcinae orstaphylococci. Bacilli may optionally be a single bacillus,diplobacilli, streptobacilli or coccobacilli. Spirals may optionally bevibrio, spirilla or Spirochetes.

With respect to the morphology of mammalial cells, this may, forexample, optionally be determined to be fibroblastic, epithelial-like,lymphoblast-like, and/or neuronal, with or without an axon.

Culture-based screening for nutrient requirements may optionally involveinoculating cells or microbes onto on into one or more different growthmedia, such as different selective media, and observing in/on whichmedia cell or microbial growth occurs, and to what extent the growthdiffers between different media.

Culture-based screening for antimicrobial sensitivity may optionallyinvolve inoculating microbes onto one or more different growth media,which may be done, for example, by streaking or plating the microbesonto a petri dish containing a suitable nutrient agar. An antimicrobialagent may then be added, which may be done, for example, by placing afilter paper disk impregnated with the antimicrobial onto the growthmedium. Several disks each containing a different antimicrobial agentmay be added onto a single petri dish. A determination may then be madeas to whether a zone of growth inhibition occurs around any of thedisk(s), and, if so, how large this zone is.

Immunohistochemical analysis may involve contacting the cells with oneor more labelled agents, such as antibodies. Thus, the presence ofspecific antigens, particularly on the cell surface of a cell ormicrobe, may optionally be tested for by using specific antibodies.Testing for antigens may also be referred to as serotyping. Theantibodies may be polyclonal or monoclonal. If the antibodies arespecific for a particular cell type, then the number of cells of thattype may be assessed. The test may optionally involve simply detectingthe presence or absence of agglutination, i.e. the formation ofcomplexes of cells/microbes and antibodies. Alternatively or inaddition, the antibodies may be labelled and the assay may involve, forexample, an enzyme-linked immunosorbent assay (“ELISA”) and/orfluorescence activated cell sorting (“FACS”).

The antibody may optionally be selected from e.g., a CD3 or CD8antibody.

Flow cytometry may optionally be used to analyse the properties of cellsor microbes in a sample or specimen, e.g., the number of cells/microbes,percentage of live cells/microbes, cell/microbe size, cell/microbeshape, and/or the presence of particular antigens on the cell/microbesurface.

Western blot hybridization may optionally be used to analyse proteinsand/or peptides.

Optionally, in situ hybridization of labelled probes to tissues,microbes and/or cells may be performed, optionally using an arrayformat. The method may be Fluorescence in situ hybridization (FISH),which may, e.g., be used to analyse chromosomal abnormalities and/or tomap genes.

Analysis of Medium Derived from a Cell Population

Optionally, medium derived from a cell population may be analysed. Thus,there is provided a method of analysis using mass spectrometry and/orion mobility spectrometry comprising:

(a) using a first device to generate smoke, aerosol or vapour from atarget culture medium derived from an in vitro or ex vivo cellpopulation;

(b) mass analysing and/or ion mobility analysing said smoke, aerosol orvapour, or ions derived therefrom, in order to obtain spectrometricdata; and

(c) analysing said spectrometric data in order to identify and/orcharacterise one or more compounds present in said target culturemedium.

Such a method may, e.g., provide information about the compounds presentin the culture medium, which may optionally in turn provide informationabout the cell population from which the culture medium was derived. Anyof the information provided herein with respect to a cell population andthe analysis thereof applies mutatis mutandis to the method of analysinga culture medium derived from a cell population.

Shotgun Lipidomic Characterization of the NCI-60 Cell Line Panel UsingRapid Evaporative Ionization Mass Spectrometry

A methodological background was established for fundamental studiesaimed at the exploration of the molecular background of REIMS-basedtissue identification. Furthermore, comprehensive shotgun lipidomicsdata on the NCI-60 cell line collection was also obtained.

According to an embodiment Rapid Evaporative Ionization MassSpectrometry (REIMS) may be applied to the shotgun lipidomicfingerprinting of cancer cell lines. Experimental data relating tovarious embodiments and details of the experimental scheme are presentedbelow.

According to an embodiment spectral reproducibility was assessed for aset of three different cell lines.

The NCI-60 cell line panel was then subjected to REIMS analysis and theresulting dataset was investigated for its distinction of differenttissue types of origin and the correlation with publicly available geneand protein expression profiles. Significant correlations between REIMSspectral features and gene expression profiles were identified and areexemplified in case of fads2 and ugcg genes.

REIMS is an attractive means to study cell lines as it involves minimalsample preparation and analysis times in the range of several seconds.

Culturing of Cell Lines

Cells were cultured in RPMI 1640 medium, with the exception of HEK andHeLa cells in the Mycoplasma study which were cultured in Gibco DMEMmedium (Invitrogen, Carlsbad, Calif., USA). In all cases, media weresupplemented with 10% (v/v) fetal bovine serum and with 2 mM glutamine,100 units/mL penicillin, and 100 mg/mL streptomycin (Invitrogen-Gibco,Carlsbad, Calif., USA). Cells were incubated in 75 cm² tissue cultureflasks at 37° C. under conditions of humidified 37° C., 5% carbondioxide atmosphere. Cell lines were regularly screened for mycoplasmacontamination using the MycoAlert™ Mycoplasma Detection Kit (Lonza GroupLtd, Basel Switzerland). At 80%-90% confluence in 75 cm² tissue cultureflasks, cells were rinsed with Phosphate Buffered Saline (PBS, pH: 7.2)solution, and were detached using 0.1% trypsin/EDTA for 10 minutes. Thetrypsin was subsequently neutralized with excess culture medium (RPMI).The cell suspension was centrifuged at 250×g for five minutes. Aftercentrifugation the cells were re-suspended and washed two times in 10 mLPBS. A third wash was performed in an Eppendorf tube with only 1 mL PBS.The cell pellets were frozen and stored at −80° C. until furtheranalysis.

Mycoplasma Infection and Treatment

Mycoplasma-infected HEK and HeLa cell lines were treated with 25 μg/mLPlasmocin™ Mycoplasma Elimination Reagent (InvivoGen, San Diego, Calif.,USA) for 14 days.

REIMS Analysis

For REIMS analysis, two handheld electrodes in the form of a forcepswere used as the sampling probe (irrigated bipolar forceps, obtainedfrom Erbe Elektromedizin, Tübingen, Germany). A Valleylab Force EZcpower-controlled electrosurgical unit (Covidien, Dublin, Ireland) wasused at 60 W power setting in bipolar mode as radiofrequency alternatingcurrent power supply (470 kHz, sinusoidal). An approximately 1.5 m long⅛ inch outer diameter, 1/16 inch inner diameter PTFE tubing (FluidflonPTFE tubing; LIQUID-scan GmbH Co. KG, Überlingen, Germany) was employedto connect the embedded fluid transfer line of the bipolar forceps withthe inlet capillary of a mass spectrometer. The inherent vacuum systemof the mass spectrometer was used for aspiration of theanalyte-containing aerosol created during analysis. This setup is shownin FIGS. 1A-1C.

As shown in FIGS. 1A-1C a sample may be provided in the form of a cellpellet 101 in an Eppendorf tube. A sampling probe 102 may be used tosample the cell pellet 101. The sampling probe 102 may be energized by aRF power supply 103. Application of a RF voltage to the sampling probe102 results in the generation of an aerosol which is transmitted viatransfer tubing 104 to the inlet 105 of a mass spectrometer.

The particular instrumental settings which were used are given in thetable below:

Parameter Setting Injection time 1000 ms Microscans 1 Ion mode negativeMass range 150-2000 Tube Lens Voltage −160 V Capillary Voltage −50 VSkimmer Voltage −24 V Capillary Temperature 250° C Automatic GainControl On AGC Target High dynamic range Resolution 50,000 at m/z 200

Mass spectrometric analysis of the cell line biomass was performeddirectly on a thawed cell pellet without further sample pre-processingsteps. 0.1-1.5 mg of cell biomass was taken up between the tips of theforceps and the two electrodes were subsequently brought into closeproximity (i.e. by pinching the biomass between the tips of theforceps). The RF power supply was triggered using a foot switch. Thecell line biomass is rapidly heated up due to its non-zero impedance andan aerosol containing charged molecular species of the analytes isproduced and transferred directly into the mass spectrometer. Multipletechnical replicates were recorded for each cell line.

Data Analysis

Raw mass spectrometric files were converted into mzML format using theMSConvert tool (part of the ProteoWizard 3.0.4043 suite) andsubsequently imported as imzML format into MATLAB (Mathworks, Natick,Mass.; http://www.mathworks.co.uk/) for data pre-processing. All REIMSspectra were linearly interpolated to a common sampling interval of 0.01Da. Recursive segment wise peak alignment was then used to remove smallmass shifts in peak positions across spectral profiles. The aligned datawere subjected to total ion count (TIC) data normalization and log-basedtransformation. Pattern recognition analysis and visualization wereperformed either in Matlab or in RStudio (Boston, Mass., USA, see alsowww.r-project.com). The mass range of m/z 150-1000 was used for dataanalysis. For self-identity experiments, the data set was filtered tokeep a reduced set of m/z values: a m/z value was kept, if thedifference between the available samples were significantly different atalpha=0.01 threshold level based on the Kruskal-Wallis test.

Ionic species in the mass spectra were identified based on exact massmeasurements (mass deviation <3 ppm) and MS/MS fragmentation patterns.

Spectral Content

Spectral content comprises fatty acids and all glycerophospholipidspecies undergoing ionization in negative ion mode, includingphosphatidic acids (PAs), phosphatidylethanolamines (PEs),phosphatidylglycerols (PGs), phosphatidylserines (PSs),phosphatidylinositols (PIs) in agreement with earlier REIMS studiesperformed on bulk tissue samples. All observed ions displayed a singlenegative charge, the vast majority by forming the quasi-molecular [M-H]−ion. In addition, [M-NH3—H]− was observed in case of PEs. Variousceramide, glycosylated ceramide, diglyceride and triglyceride specieswere detected as [M+Cl]− ions.

Reproducibility Dataset

For each cross-validation run, a principal component analysis (PCA)transformation of the training data set with pre-determined number ofprincipal components (PCs) was calculated in R and a prediction scorewas calculated for each test sample using the 3 nearest neighbor (3-NN)method. The training data in the ‘reproducibility set’ was selected asfollows: for each measurement day, a cell line with defined passage (p)and flask number (A/B) was kept as part of the training data (e.g., HeLap4 A) if samples were available from at least two different biologicalreplicates (i.e. A1-3). Such sets from each of the three cell lines werecombined randomly to produce balanced training data where each cell lineis represented by similar number of samples. All of the remainingsamples constituted the test set.

FIG. 2 shows the experimental scheme used for assessment of REIMSspectral reproducibility. In particular, flasks A and B are shown.

All of the remaining samples constituted the test set. The resultingcross-validation results can be seen in the table below.

The table below shows cross-validation results for SNB-19, HeLa andMES-SA cell lines based on PCA model comprising the first 4 principalcomponents and using 3 nearest neighbour as classifier:

predicted kept in test set HeLa MES-SA SNB-19 correct HeLa p6 A day2 (9samples) 63 — — 100% MES-SA p8 A day1 (9 samples)  2 57 —  97% SNB-19 p4A day1 (9 samples) — — 54 100% HeLa p4 B day2 (9 samples) 62  1 —  98%MES-SA p10 B day1 (9 samples) 17 42 —  71% SNB-19 p4 B day2 (7 samples)— — 56 100% HeLa p4 A day1 (9 samples) 49 14 —  78% MES-SA p10 A day3(12 samples) — 56 — 100% SNB-19 p6 A day2 (7 samples) —  1 55  98%

Cross-validations were performed based on a PCA model created of atraining set as described elsewhere herein. Following this procedure,three different training sets were generated and subjected to PCAanalysis. These training sets comprised between 25-28 sampling points,which represent <15% of the overall sample points (n=203). Thecomposition of the training sets and corresponding cross-validationresults are shown in the Table above. Consistently, the cross-validationresults show >98% correct classification for SNB-19 samples, with only asingle misclassification observed in case of the third training set,which was tentatively associated with the unequal sample size for MES-SA(n=12) and SNB-19 (n=7). Unequal sample size is known to lead to a biasin PCA calculations towards the larger sample subset, which explains themisclassification of SNB-19 cells as MES-SA.

Spectral Reproducibility in NCI-60 Cell Line Panel

The table below shows the different cell lines present in the NCI-60cell line panel and respective number of replicates which were used.

No of No of Tissue biological technical of origin Cell line replicatesreplicates Breast BT549 1 4 HS-578-T 1 6 MCF-7 1 12 MDA-MB-231 2 10 (6 +4) MDA-MB-468 1 TD-47-D 1 11 CNS SF268 2 18 (8 + 10) SF295 2 18 (8 + 10)SF539 1 8 SNB-19 1 10 (3) (10 + ? + ?) SNB-75 not measured U251 1 9Colon COLO-205 1 10 HCC2998 1 7 HCT-15 1 7 HCT-116 1 11 HT-29 2 9 (5 +4) KM-12 1 7 SW-620 1 12 Leukaemia CCRF-CEM 1 10 HL-60 1 7 K562 1 9 (2)MOLT-4 1 6 RPMI-8226 1 5 SR 1 8 Melanoma LOX-IMVI 1 9 M14 2 15 (7 + 8)Malme-3M 1 6 MDA-MB-435 2 14 (4 + 10) SK-MEL-2 1 10 SK-MEL-5 1 8SK-MEL-28 1 9 UACC62 1 6 UACC257 1 9 Non small cell A549 1 10 lungcancer EKVX 1 9 HOP-62 1 6 HOP-92 1 13 NCI-H23 1 11 NCI-H226 1 9NCI-H322M 1 7 NCI-H460 1 9 NCI-H522 1 9 Ovarian IGROV-1 2 8 (5 + 3)NCI-ADR-RES 1 9 OVCAR-3 1 6 OVCAR-4 1 7 OVCAR-5 1 10 OVCAR-8 1 8 SK-OV-31 9 Prostate DU-145 1 9 PC-3 1 11 Renal 786-0 1 9 A498 1 11 ACHN 1 10CAKI-1 1 6 RXF393 1 6 SN-12-C 1 8 TK-10 1 9 UO31 1 7

Biological replicates were analysed in of six out of the 58 cell linesused in this study. To assess the specificity of the REIMS spectralpatterns toward individual cell lines, cross-validations were performedby omitting one replicate from the PCA model building process, and thenprojecting it into the resulting data space and classifying each datapoint based on its three nearest neighbour.

FIG. 3 shows a PCA plot of NCI-60 cell line panel, m/z 150-1000, withreplicates highlighted.

The table below shows the results of cross-validation results of the PCAmodel shown in FIG. 3 . Cross-validations were performed as describedabove leaving a whole biological replicate out at a time. Goodidentification results (100%) were obtained for leukemia K562, melanomaMDA-MB-231 and MDA-MB-435 cell lines. One replicate of CNS SF295 wasclassified correctly. However, a second one is only correctly assignedin case of 50% of the data points. The misclassified samples were allwrongly assigned as SNB-19, another CNS cell line. The same is true forIGROV-1, of which the misclassified replicate falls into OVCAR-8,another ovarian cancer cell line. In the case of small sample number,correct classification becomes more difficult, but still the cellidentity of a large proportion of HT-29 samples were correctlypredicted.

Overall, the good prediction results further supports that REIMSspectral profiles can be used characterise and classify human cell linesamples.

Cross-Validation Results for Replicates in NCI-60 Dataset

In the case of some cell lines, two biological replicates have beenmeasured. For these cell lines both replicate was retained one by oneand supplemented with samples from all other cell lines as the trainingset. The other biological replicate constituted the test set.

The table below shows cross-validation results for replicated NCI-60cell lines based on PCA model of the entire NCI-60 dataset comprisingthe first 10 principal components. Three nearest neighbour asclassifier:

kept in test set predicted correct K562 P5 (9 samples) LE:K562 (9) 100%K562 P11 (9 samples) LE:K562 (9) 100% MDA-MB-231 (6 samples) BR:MDAMB231(4) 100% MDA-MB-231 P10 (4 samples) BR:MDAMB231 (6) 100% SF295 1 (10samples) CNS:SF295 (4), CNS:SNB19 (4)  50% SF295 2 (8 samples) CNS:SF295(10) 100% M14 (7 samples) ME:M14 (5), ME:MDAMB435 (1),  63% ME:SKMEL28(2) M14 (2) (8 samples) ME:M14 (1), ME:MDAMB435 (6) 14% MDA-MB-435 (10samples) ME:MDAMB435 (4) 100% MDA-MB-435 P6 (4 samples) ME:MDAMB435 (10)100% HT-29 (5 samples) CO:HT29 (3), OV:OVCAR5 (1) 75% HT-29 P3 (4samples) CO:HT29 (4) CO:COLO205 (1) 80% IGROV-1 (5 samples) OV:IGROV1(3) 100% IGROV-1 P9 (3 samples) OV:OVCAR8 (4) RE:UO31 (1) 0%Correlation with Gene Expression Data

Gene expression for the NCI-60 cell line panel was obtained from theCellMiner online data query tool. For each available gene and filteredm/z value the gene expression and the binned signal intensity across thecell lines was correlated using Pearson's correlation coefficient with1000 iterations. The bootstrapped correlation value was defined as thelower 95% confidence interval level of the 1000 iterations, resulting ina total of 26065 (genes)×17878 (filtered m/z)=465990070 values.

Bootstrapped Correlation Analysis

For each available gene and binned m/z value with sufficient variance abootstrapped correlation coefficient was calculated, resulting in atotal of 26065×5452=142106380 correlation values. In the case of eachgene—m/z value pair, the gene expression and the binned signal intensityacross the 58 cell lines was correlated using Pearson's correlationcoefficient with 1000 iterations. The bootstrapped correlation value wasdefined as the lower 95% confidence interval level of the 1000iterations.

Comparison of Cell Line and Bulk Cancer Tissue Spectra

FIG. 4 shows the results of a comparison of cell line and bulk cancertissue spectra, indicating mass to charge ratio values that were foundto be significantly increased in either bulk cancerous tissue or cancercell lines.

Correlation of REIMS Spectral Data with Protein Expression Data

Protein expression data was obtained from online source of theTechnische Universität München, Germany (Gholami et al., supra). Thefads2 gene encodes the fatty-acid desaturase 2 protein. However, forthis protein data was only available in case of 29 members of the NCI-60cell line panel.

To assess the agreement of data for gene and protein expression, bothwere plotted as a function of phospholipid species PE(38:3) and theratio of PE(38:3)/PE(38:2).

FIG. 5A shows phospholipid species PE(38:3)/PE(38:2) peak intensityratio and FIG. 5B shows PE(38:3) peak intensity as a function of fads2protein expression.

Correlation of Glycosylated Lipids with Ugcg Gene

Fragmentation spectra were recorded for peak set at m/z=842-846 recordedusing a Waters Xevo G2-XS Q-ToF® instrument with a collision energy setat 35 eV. Fragmentation spectra showed similar behaviour and thussimilar structural backbone. The main fragments observed are due to lossof HCl (Δm=36 Da) and loss of a hexose moiety HCl (Δm=198 Da). The lossof the hexose moiety as major fragmentation pathway agrees well withspectra of reference standards as found at Lipid Maps for thecorresponding [M-H]⁻ ion. No differentiation can be made betweenglycosylated and galactosylated ceramides.

Safety Considerations

To avoid any negative health impact originating from aerosolized cancercells, the analysis site was enclosed into a Class safety level glovebox compartment equipped with UV light source and HEPA filters.

Robustness of REIMS Spectral Profiles

In order to show that REIMS spectral patterns are reproducible andsufficiently specific to differentiate between different human cancercell lines, three different cell lines (HeLa-cervical adenocarcinoma,MES-SA—uterine sarcoma and SNB-19-glioblastoma) were analyzed in anexperiment designed to test spectral reproducibility.

The experimental scheme accounts for variance introduced by differentculture batches or the passage number and for analytical varianceintroduced by the multiple measurements. Reference is made to FIG. 2 .

Replicates were randomly analyzed over three analysis days in order toassess the analytical variance and robustness of a REIMS-based lipidprofiling method. In addition, the influence of freeze-thaw cycles onspectral variance was investigated and was found to be insignificant.

Raw REIMS mass spectrometric profiles of the three cell lines showsignificant similarities as is apparent from FIG. 6A. The main spectralcontent comprises predominantly glycerophospholipid-type membrane lipidcomponents such as phosphatidylethanolamines (PEs),phosphatidylinositols (PIs), phosphatidylglycerols (PGs), phosphatidicacids (PAs) and phosphatidylserines (PSs) as well as other complexlipids including ceramides and glycosylated ceramide species. Allobserved ions displayed a single negative charge, the vast majority byforming the quasi-molecular [M-H]⁻ ion. In addition, [M-NH₃—H]⁻ wasobserved in case of PEs. Sphingolipid species were detected as [M+Cl]⁻ions.

For clinically oriented applications, REIMS spectral profiles werelargely analyzed using supervised multivariate statistical analyses suchas linear discriminant analysis (LDA) to explore the differentiation ofvarious tissue types or healthy and diseased tissues.

In the experimental results presented below the analysis was restrictedto exploratory unsupervised analysis methods to confirm that REIMSprofiles would reproducibly cluster into different groups correspondingto cell line identities.

PCA of the REIMS profiles defined three clusters, as shown in FIG. 6B,corresponding to the three cell lines. SNB-19 cells are clearlydifferentiated from HeLa and MES-SA cells along the first principalcomponent. The second and the third principal component allow the fullseparation of HeLa and MES-SA cell lines from each other. A slightseparation along the second principal component due to passage numberswas observed for HeLa and SNB-19, however, analytical and biologicalvariances were found to be small compared to the inherent spectraldifferences of the cell lines. These results suggest that although thereis an expected biological and analytical variance, the REIMS spectralprofiles obtained from cell line pellets show sufficient reproducibilityand specificity to characterize and distinguish human cancer cell lines.

REIMS Profile of the NCI-60 Cell Line Panel

Following confirmation that REIMS spectral patterns are able todistinguish between three cancer cell lines, the entire NCI-60 panelconsisting of 60 human different cancer cell lines was profiled. Basedon amount of available biomass after culture, 4 to 15 individualmeasurement points were made for each cell line. Several biologicalreplicates were also included (the detailed sample set is given in atable above).

Hierarchical cluster analysis as shown in FIG. 7 and principal componentanalysis (as shown in FIG. 3 ) of the filtered sample averages indicatedthat the 60 cells are characterized by unique REIMS profiles.

Biological replicates showed the expected level of similarity asindicated by the cluster analysis (FIG. 7 ) or the cross-validationresults given in table above.

Profiling studies revealed that the MDA-MB-435 cells more closelyresembled melanoma cell lines than the other breast tumor lines (Ross,Nat Genet 2000).

Consistent with gene expression, SNP and karyotype analyses, the REIMSprofiles also confirmed that MDA-MB-435 and M14 are of the same origin(FIG. 7 , arrows).

Karyotyping has also found that the NCI-ADR-RES is in fact a drugresistant derivative of OVCAR-8.

As shown in FIG. 7 , these cell lines (indicated by arrows) also showclose similarity based on their REIMS profiles. Taken together, theseresults confirm that REIMS profiles are strongly associated with thebiological identity of cancer cell lines.

Gene and protein expression patterns of the NCI-60 panel were found tocorrelate with tissue types whereas metabolomic signatures did notdifferentiate between tissue origins.

Clustering of the cell lines based on their REIMS lipid profile showedextensive heterogeneity within most tissue types, except for melanomasamples (FIG. 7 ).

REIMS profiles of the NCI60 panel were subsequently compared to bulkcancer samples of ovarian and colon adenocarcinomas analyzed using thesame experimental setup. A PCA plot of the resulting dataset is shown inFIG. 8 and reveals clear differences between cell lines and bulk tissuespecimens along the first principal component suggesting strongdifferences among their membrane lipid composition. A tentativeseparation according to tissue type of origin can be observed for bothcell lines and tissue specimens, although more pronounced in the latter.Only a small number of tissue specimens (n=4) were available in case ofovarian tumors, but based on previous studies a significant increase inseparation power can be expected for larger sample sets. Nevertheless,the direction of separation is similar in both cases, indicating similarlipidomic differences.

Representative spectral profiles of both ovarian and colon cancer tissueand cell line samples are shown in FIGS. 9A-9D.

FIG. 9A shows the mass spectral profile for bulk ovarian cancer tissue,FIG. 9B shows a corresponding mass spectral profile for ovarian cancercell line OVCAR-3, FIG. 9C shows a mass spectral profile for bulkcolorectal cancer tissue and FIG. 9D shows a mass spectral profile forcolon cancer cell line HCT-15.

Bulk tissue samples (c.f. in vitro cultured cell lines) display largeramounts of long-chain phosphatidylinositols such as PI (38:4) at m/z885.55. Similar trends were observed in case of certainphosphatidylethanolamines. For example, the peaks detected in the massrange of m/z 790-794 corresponding to PE(40:6)-PE(40:4) species or thoseoccurring at m/z 766.54 and 768.55 corresponding to PE(38:4) andPE(38:3), respectively. On the other hand, m/z 645.45, corresponding toPA (32:1), was found in significantly higher proportions in cell lines.

The characteristic differences in lipid composition may be due to theuniform lipid content of the culturing medium, which does notrecapitulate the complex lipid source of real tumors that rely ondietary and liver-synthesized lipids as well as de-novo lipid synthesis(see FIG. 8 for a statistical analysis of distinct spectral featuresassociated with cell lines and bulk tumors).

Correlation with Gene Expression Data

Cells established from different tissues were found to be more similarto each other than to the matching clinical samples. The limitations ofcell lines as surrogates for clinical tumors are well known, but anadvantage of the NCI-60 panel is the wealth of pharmacological databased on exposure of the cells to large numbers of drugs and otherchemical compounds. The NCI-60 has been characterized more extensivelythan any other set of cell lines. The relationship of global patternshas yielded valuable biological insight and revealed the target andmechanism of action of anticancer compounds.

The availability of the REIMS and gene expression profiles enablesidentification of the relation of lipidome and transcriptome features inthe NCI-60 cell lines.

In order to identify lipid-gene associations, the REIMS dataset wascorrelated to gene expression data obtained via the CellMiner onlinequery tool. Using an exhaustive strategy, the patterns of each binnedm/z intensity were correlated with each gene's expression profile acrossthe cell lines as described above.

Strong positive correlation was observed between the expressionintensities of several genes playing an important role in lipidmetabolism and the lipid profiles of the 60 cells. Positively associatedlipid-gene pairs may reflect a causally relevant relation if the geneproduct is a rate-limiting factor in the production of the given lipidspecies. For example, the expression pattern of the fads2 gene was foundto be significantly correlated with the abundance of several lipids, asreflected by the intensity of m/z peaks within the REIMS data set (FIG.10 ). The mammalian A6-desaturase encoded by the fads2 gene catalyzesthe biosynthesis of polyunsaturated fatty acids from precursor essentialfatty acids such as linoleic acid C18:2n−6 and linolenic acid C18:3n−3.Other reported substrates include C16:0, C20:2n−6, C20:3n−3, C24:4n−6and C24:5n−3.

Peaks showing significant correlation to fads2 gene expression could beputatively assigned to various unsaturated glycerophospholipids. One ofthe correlated binned peaks (corresponding to m/z=768.54) was attributedto a peak centering at m/z=768.5578, which in turn was assigned toPE(38:3) based on exact mass measurements, and PE(18:0/20:3) based onMS/MS data.

Assuming that both enzyme substrate and product would be incorporatedinto the same phospholipid species, the putative substrate can beindirectly attributed to PE(38:2) at m/z=770.5733. Indeed, it was foundthat the relative abundance of PE(38:2) and PE(38:3) shows significantcorrelation to fads2 gene expression levels across the NCI-60 cells(FIG. 11B).

Correlation of PE(38:3)/PE(38:2) intensity ratio or PE(38:3) abundancewas verified using protein expression data as shown and described abovein relation to FIG. 5 . Thus, consistently with the enzymatic activityof the mammalian A6-desaturase, cells expressing higher fads2 levels areexpected to have lower PE(38:2) and higher PE(38:3) levels. Raw REIMSprofiles of HL-60 and HT-29 cells (showing high and low fads2 expressionlevels, respectively) verify this hypothesis: in HT-29 cells, PE(38:3)accounts for approximately 15% of relative abundance compared toPE(38:2), for HL-60 the relative abundance increases to approximately40% of the PE(38:2).

Strong correlations have also been found in case of ugcg gene expression(FIG. 12 ). This gene encodes the UDP-glucose ceramideglucosyltransferase (UGCG) enzyme, which catalyzes the firstglycosylation step in glycosphingolipid biosynthesis, having ceramidesand UDP-glucose as substrates. Glycosylated lipids are enriched in lipidrafts/lipid micro-domains and play fundamental roles in a variety ofcellular processes. Positive correlations were observed for severalsignals between m/z=700-900 and their respective isotopes as shown inFIG. 12B and the table below.

Based on exact mass measurements and MS/MS experiments, these specieswere tentatively identified as homologous mono-hexosylated ceramides inform of [M+Cl]⁻ ions. Isotopic patterns recorded for these species andMS/MS data support the presence of a chloride in the adduct ion. Allions show analogous fragmentation behavior, displaying a loss of thechloride ion (Δm=35 Da) and additional loss of the hexose moiety (Δm=198Da, loss of HCl and C₆H₁₀O₅). Using tandem mass spectrometry only it isnot possible to differentiate between glucosylated and galactosylatedceramide species as they exhibit identical fragment ions. Exemplarytandem mass spectra and mass spectrometric signal intensities as afunction of gene expression numbers can be found in FIG. 13 and FIG. 14, respectively. No negative correlation was observed for the ugcg geneexpression with the precursor ceramide species nor the ratio ofglucosylceramides to precursor ceramides. This is associated with thefact that ceramides are ubiquitous precursors in sphingolipidbiosynthesis and there are numerous biosynthetic pathways whereceramides act as substrates, intermediates or products.

The following table shows spectrometric signals that show strongpositive correlation with the ugcg gene expression for the NCI-60dataset:

Correlation Exp. mass Exact mass Δppm Tentative ID Formula Adductcoefficient 734.5355 734.5343 0.2 GlyCer(d18:1/16:0) C₄₀H₇₇NO₈ [M + Cl]⁻0.552 818.6295 818.6282 0.2 GlyCer(d18:1/22:0) C₄₆H₈₉NO₈ [M + Cl]⁻ 0.662842.6312 842.6332 −0.2 GlyCer(d18:1/24:2) C₄₈H₈₉NO₈ [M + Cl]⁻ 0.602844.6451 844.6439 0.1 GlyCer(d18:1/24:1) C₄₈H₉₁NO₈ [M + Cl]⁻ 0.668846.6627 846.6595 0.4 GlyCer(d18:1/24:0) C₄₈H₉₃NO₈ [M + Cl]⁻ 0.688872.6733 872.6752 −0.2 GlyCer(d18:1/26:1) C₅₀H₉₅NO₈ [M + Cl]⁻ 0.707

Other lipid species detected at m/z=860.6411, 862.6552 and 864.6775showed positive correlations with the expression profiles of a number ofgenes encoding proteins involved in sphingolipid synthesis, e.g., fa2h,a gene encoding a protein that catalyzes the synthesis of2-hydroxysphingolipids. Further genes include the degs2 gene, whichencodes the Delta(4)-desaturase, sphingolipid 2 protein, and the nr1h3gene, which encodes the Liver X receptor alpha protein. Liver Xreceptors are part of the control system for transcriptional programsinvolved in lipid homeostasis and inflammation and might thus indirectlyinfluence the increase of certain sphingolipid species.

Analysis of Mycoplasma-Infected Cell Lines

Cell cultures frequently get infected by Mycoplasma, a genus of bacteriathat lack a cell wall around their cell membrane. Mycoplasma infectioncan alter many physiological processes and thus lead to misleadingexperimental results if a study is performed using infected cells.Plasmocin® (InvivoGen, San Diego, Calif., USA) is a commerciallyavailable antibiotic treatment that is frequently used to eradicatemycoplasma infection in cell cultures.

REIMS profiles of Mycoplasma-free, Mycoplasma-infected and Plasmocin®cured HeLa and HEK cell lines were recorded.

The data was pre-processed as described for the NCI-60 dataset. For eachHeLa and HEK cell lines, ANOVA tests were performed to determinesignificant differences between Mycoplasma positive and negativesamples.

Adjusted p-values were obtained using the adaptive Benjamini-Hochberg(BH) procedure to correct for multiple testing.

FIG. 26 shows the time-dependent raw intensities in course of Plasmocin®treatment of the mycoplasma infection in case of m/z=747.5183.

Sampling point #1 corresponds with day 1 and the original mycoplasmapositive or negative sample and sampling point #2 corresponds with day 2and the addition of Plasmocin® antibiotic. Sampling point #3 correspondswith day 3. Sampling point #4 corresponds with the removal of Plasmocin®antibiotic. Sampling point #5 corresponds with all samples beingPlasmocin® free.

As shown in FIG. 27A, 23 and 386 binned m/z signals were significantlyhigher in Mycoplasma-infected HEK and HeLa cells, respectively. Thehigher number of significantly increased peaks may be explained with ahigher number of sampling points (contributing to higher power insignificance testing) or may reflect the increased reactivity of HeLacells to Mycoplasma infection. Interestingly, we found no signalsshowing reduced intensity in Mycoplasma-infected cell lines (p=0.15).Table 15 lists the annotation of the 18 m/z signals that were found tobe significantly increased across all Mycoplasma-infected cells(p=1.37E−20). As an example, changes in the intensity of m/z 819.52(identified as PG(40:7) based on exact mass measurements) are shown inMycoplasma-free, Mycoplasma-infected and Plasmocin™-treated HEK and HeLacells (FIGS. 28A and B). This m/z value, along with the signalcorresponding to its isotope, was found to be increased inMycoplasma-infected HeLA and HEK cells, whereas the intensity returnedto pre-infection levels upon successful Plasmocin™ treatment. Similarresults were obtained for the other m/z signals shown in Table 15.

In the 18 dimensional space of these m/z signals, Mycoplasma-infectedand Mycoplasma-free HEK and HeLa samples were analyzed by PCA (FIG.27B). The first principal component (PC1) reveals differences betweenthe two different cell lines while the second PC separatesMycoplasma-free and Mycoplasma-infected samples.

Trends in the spectral intensities of these species were found to besignificantly different in case of the healthy and infected cell lines,suggesting that the lipid metabolism has been perturbed by mycoplasmainfection. The above approach demonstrates the applicability of theREIMS method to study changes during Mycoplasma infection and aspossible use for Mycoplasma screening.

CONCLUSION

The above described experiments demonstrate the applicability of aREIMS-based shotgun lipidomic characterization approach for humancancerous cell lines. Individual cancer cell lines were found to exhibitreproducible and cell line-specific spectral profiles while spectracould be acquired in less than five seconds. This does not only allowrapid identification of cell lines based on their spectral fingerprint,but also detailed characterization of membrane lipid composition inorder to study the changes in the cell membrane composition in differentcancer phenotypes.

By continued analysis of the correlation between REIMS spectral data andgene and protein expression data, the sensitivity of the REIMS spectralmethod for tumor phenotypic characterization can be assessed in detail.In addition, this technique enables investigations to be performed ofgene knock-out models for changes in lipid metabolism or the effects offeeding experiments with stable isotope-labelled nutrient sources.

TABLE 1 Table of biomarkers: phospholipids and their spectrometricsignals Identified phospholipids detected in the mass range m/z =600-900 for all analysed microbial species. Only phospholipids withrelative abundances >5% and only the most abundant acyl chaincombination were included. Solid growth media on which bacteria weregrown is given in parentheses. ID based solely on exact mass when lipidcomposition given as sum carbon number rather than individual acylchains. Nominal mass C. koseri E. coli K. pneumoniae P. mirabilis P.aeruginosa S. marascens S. aureus S. agalactiae S. pyogenes m/z (CBA)(CBA) (LB) (MCC) (LB) (MCC) (CBA) (CBA) (CBA) 645 PA(32:1)* 659 PA(16:0/PA(16:0/ PA(16:0/ 17:1) 17:1) 17:1) 661 PA(33:0)* 665 PG(12:0/ 16:0) 671PA(34:2)* 673 PA(16:0/ PA(16:0/ PA(16:0/ 18:1) 18:1) 18:1)* 675 PG(15:0/PG(30:0- 15:0-H₂O) H₂O)* 688 PE(16:1/ PE(16:1/ 16:0) 16:0) 691 PG(14:0/16:1) 693 PG(16:0/ PG(16:0/ PG(15:0/ PG(15:0/ PG(14:0/ 14:0) 14:0) 15:0)15:0) 16:0) 697 PA(36:3)* 699 PA(18:1/ 18:1)* 701 PG(32:1)- PG(32:1)-H₂O* H₂O* 702 PE(16:0/ PE(16:0/ PE(16:0/ PE(16:0/ PE(16:0/ 17:1) 17:1)17:1) 17:1) 17:1) 707 PG(15:0/ 16:0) 716 PE(18:1/ PE(18:1/ PE(18:1/PE(17:0/ 16:0) 16:0) 16:0) 17:1) 717 PG(32:2)* PG(16:1/ 16:1) 719PG(16:1/ PG(16:1/ PG(16:0/ PG(16:0/ PG(16:0/ PG(16:0/ PG(16:0/ PG(16:0/16:0) 16:0) 16:1) 16:1) 16:1) 16:1) 16:1) 16:1) 721 PG(15:0/ PG(15:0/PG(16:0/ 17:0) 17:0) 16:0) 725 PA(16:1/ 18:2) 727 PG(16:1/ 18:1)-H₂O 729PG(16:0/ PG(16:0/ 18:1)-H₂O* 18:1)-H₂O 730 PE(16:0/ 19:1) 733 PG(16:0/PG(16:0/ PG(16:0/ PG(16:0/ PG(16:0/ PG(16:0/ 17:1) 17:1) 17:1) 17:1)17:1) 17:1) 735 PG(15:0/ 18:0) 743 PG(16:0/ PG(16:1/ 18:3) 18:2) 745PG(16:1/ PG(16:1/ PG(16:1/ PG(16:1/ PG(16:1/ PG(16:0/ PG(16:1/ 18:1)18:1) 18:1) 18:1) 18:1) 18:2)* 18:1) 747 PG(16:0/ PG(16:0/ PG(16:0/PG(16:0/ PG(16:0/ PG(16:0/ PG(16:0/ PG(16:0/ 18:1) 18:1) 18:1) 18:1)18:1) 18:1) 18:1) 18:1) 749 PG(15:0/ PG(15:0/ PG(16:0/ 19:0) 19:0)18:1)* 752 759 PG(17:1/ PG(17:1/ PG(17:1/ PG(17:1/ 18:1) 18:1) 18:1)18:1) 761 PG(16:0/ PG(16:0/ PG(16:0/ PG(16:0/ PG(16:0/ 19:1) 19:1) 19:1)19:1) 19:1) 763 PG(15:0/ 20:0) 770 PE(38:2)* 771 PG(36:3)* PG(18:1/18:1)* 773 PG(18:1/ PG(18:1/ PG(17:1/ PG(17:1/ PG(18:1/ PG(36:2)*PG(18:1/ 18:1) 18:1) 19:1) 19:1) 18:1) 18:1) 775 PG(36:1)* PG(18:0/18:1) 787 PG(18:1/ 19:1) 801 PG(19:1/ 19:1) *Signal intensity notsufficient to obtain meaningful MS/MS data; Abbreviations: PG =phosphatidylglycerol, PE = phosphatidylethanolamine, CBA = Columbiablood agar, LB = lysogenic broth agar, MCC = McConkey agar.

TABLE 2 Table of biomarkers: cardiolipins and their spectrometricsignals Cardiolipin species that were identified for Staphylococcusepidermidis ATCC 12228. Exact Sum mass Exp. Mass Compound formula [M −H]− mass Deviation CL(62:0) C₇₁H₁₃₈O₁₇P₂ 1323.9335 1323.9268 5.0 ppmCL(63:0) C₇₂H₁₄₀O₁₇P₂ 1337.9492 1337.9426 4.9 ppm CL(64:0) C₇₃H₁₄₂O₁₇P₂1351.9649 1351.9601 3.6 ppm CL(65:0) C₇₄H₁₄₄O₁₇P₂ 1365.9806 1365.97583.5 ppm CL(66:0) C₇₅H₁₄₆O₁₇P₂ 1379.9962 1379.9913 3.5 ppm CL(67:0)C₇₆H₁₄₈O₁₇P₂ 1394.0119 1394.0070 3.5 ppm CL(68:0) C₇₇H₁₅₀O₁₇P₂ 1408.02751408.0238 2.6 ppm CL(69:0) C₇₈H₁₅₂O₁₇P₂ 1422.0432 1422.0400 2.3 ppmCL(70:0) C₇₉H₁₅₄O₁₇P₂ 1436.0588 1436.0561 1.9 ppm CL(71:0) C₈₀H₁₅₆O₁₇P₂1450.0745 1450.0748 0.2 ppm CL(72:0) C₈₁H₁₅₈O₁₇P₂ 1464.0900 1464.09704.8 ppm

TABLE 3 Table of biomarkers: mycolic acids and their spectrometricsignals Identified mycolic acids as detected in differentCorynebacterium species. Exact Sum mass Exp. Mass Compound formula [M −H]− mass Deviation MS/MS fragments alpha-Mycolic acid C₂₈H₅₅O₃439.415669 439.4159 0.5 ppm — C28:0 alpha-Mycolic acid C₃₀H₅₉O₃467.446969 467.4473 0.7 ppm 227 (C14:0), 255 C30:0 (C16:0) alpha-Mycolicacid C₃₂H₆₁O₃ 493.462619 493.4634 1.6 ppm — C32:1 alpha-Mycolic acidC₃₂H₆₃O₃ 495.478269 495.4786 0.7 ppm 255 (C16:0) C32:0 alpha-Mycolicacid C₃₄H₆₃O₃ 519.478269 519.4788 1.0 ppm — C34:2 alpha-Mycolic acidC₃₄H₆₅O₃ 521.493919 521.4942 0.5 ppm 255 (C16:0), 281 C34:1 (C18:1)alpha-Mycolic acid C₃₆H₆₇O₃ 547.509569 547.5102 1.2 ppm 281 (C18:1)C36:2

TABLE 4 Table of biomarkers: mycolic acids and their spectrometricsignals Identified mycolic acids as detected in Rhodococcus species.Exact Mass Sum mass Exp. De- Compound formula [M − H]− mass viationalpha-Mycolic acid C28:0 C₂₈H₅₆O₃ 439.4157 439.4159 0.5 ppmalpha-Mycolic acid C30:1 C₃₀H₅₈O₃ 465.4313 465.4315 0.4 ppmalpha-Mycolic acid C30:0 C₃₀H₆₀O₃ 467.4470 467.4472 0.4 ppmalpha-Mycolic acid C31:1 C₃₁H₆₀O₃ 479.4470 479.4473 0.6 ppmalpha-Mycolic acid C31:0 C₃₁H₆₂O₃ 481.4626 481.4630 0.8 ppmalpha-Mycolic acid C32:2 C₃₂H₆₀O₃ 491.4470 491.4475 1.0 ppmalpha-Mycolic acid C32:1 C₃₂H₆₂O₃ 493.4626 493.4634 1.6 ppmalpha-Mycolic acid C32:0 C₃₂H₆₄O₃ 495.4783 495.4786 0.6 ppmalpha-Mycolic acid C33:2 C₃₃H₆₂O₃ 505.4626 505.4630 0.8 ppmalpha-Mycolic acid C33:1 C₃₃H₆₄O₃ 507.4783 507.4785 0.4 ppmalpha-Mycolic acid C33:0 C₃₃H₆₆O₃ 509.4939 509.4943 0.8 ppmalpha-Mycolic acid C34:3 C₃₄H₆₂O₃ 517.4626 517.4632 1.2 ppmalpha-Mycolic acid C34:2 C₃₄H₆₄O₃ 519.4783 519.4788 1.0 ppmalpha-Mycolic acid C34:1 C₃₄H₆₆O₃ 521.4939 521.4944 1.0 ppmalpha-Mycolic acid C34:0 C₃₄H₆₈O₃ 523.5096 523.5100 0.8 ppmalpha-Mycolic acid C35:3 C₃₅H₆₄O₃ 531.4783 531.4784 0.2 ppmalpha-Mycolic acid C35:2 C₃₅H₆₆O₃ 533.4939 533.4946 1.3 ppmalpha-Mycolic acid C35:1 C₃₅H₆₈O₃ 535.5096 535.5100 0.7 ppmalpha-Mycolic acid C35:0 C₃₅H₇₀O₃ 537.5252 537.5259 1.3 ppmalpha-Mycolic acid C36:3 C₃₆H₆₆O₃ 545.4939 545.4944 0.9 ppmalpha-Mycolic acid C36:2 C₃₆H₆₈O₃ 547.5096 547.5102 1.1 ppmalpha-Mycolic acid C36:1 C₃₆H₇₀O₃ 549.5252 549.5260 1.5 ppmalpha-Mycolic acid C36:0 C₃₆H₇₂O₃ 551.5409 551.5424 2.7 ppmalpha-Mycolic acid C37:3 C₃₇H₆₈O₃ 559.5096 559.5102 1.1 ppmalpha-Mycolic acid C37:2 C₃₇H₇₀O₃ 561.5252 561.5257 0.9 ppmalpha-Mycolic acid C37:1 C₃₇H₇₂O₃ 563.5409 563.5418 1.6 ppmalpha-Mycolic acid C37:0 C₃₇H₇₄O₃ 565.5565 565.5573 1.4 ppmalpha-Mycolic acid C38:4 C₃₈H₇₄O₃ 571.5096 571.5098 0.3 ppmalpha-Mycolic acid C38:3 C₃₈H₇₄O₃ 573.5252 573.5261 1.6 ppmalpha-Mycolic acid C38:2 C₃₈H₇₄O₃ 575.5409 575.5415 1.0 ppmalpha-Mycolic acid C38:1 C₃₈H₇₄O₃ 577.5565 577.5579 2.4 ppmalpha-Mycolic acid C39:2 C₃₈H₇₆O₃ 589.5565 589.5578 2.2 ppm

TABLE 5 Table of biomarkers: mycolic acids and their spectrometricsignals Identified mycolic acids as detected in Nocardia species. ExactMass Sum mass Exp. De- Compound formula [M − H]− mass viationalpha-Mycolic acid C48:3 C₄₈H₉₀O₃ 713.6817 713.6797 2.8 ppmalpha-Mycolic acid C48:2 C₄₈H₉₂O₃ 715.6974 715.6959 2.1 ppmalpha-Mycolic acid C50:3 C₅₀H₉₄O₃ 741.7130 741.7114 2.2 ppmalpha-Mycolic acid C50:2 C₅₀H₉₆O₃ 743.7287 743.7285 0.3 ppmalpha-Mycolic acid C52:3 C₅₂H₉₄O₃ 769.7443 769.7430 1.7 ppmalpha-Mycolic acid C52:2 C₅₂H₉₆O₃ 771.7600 771.7588 1.6 ppmalpha-Mycolic acid C53:3 C₅₃H₉₆O₃ 783.7600 783.7596 0.5 ppmalpha-Mycolic acid C53:2 C₅₃H₉₄O₃ 785.7756 785.7754 0.3 ppmalpha-Mycolic acid C54:4 C₅₄H₉₆O₃ 795.7600 795.7594 0.8 ppmalpha-Mycolic acid C54:3 C₅₄H₉₈O₃ 797.7756 797.7739 2.1 ppmalpha-Mycolic acid C54:2 C₅₄H₁₀₀O₃ 799.7913 799.7902 1.4 ppmalpha-Mycolic acid C55:4 C₅₄H₁₀₂O₃ 809.7756 809.7748 1.0 ppmalpha-Mycolic acid C55:3 C₅₄H₁₀₄O₃ 811.7913 811.7907 0.7 ppmalpha-Mycolic acid C55:2 C₅₄H₁₀₆O₃ 813.8069 813.8061 1.0 ppmalpha-Mycolic acid C56:5 C₅₆H₁₀₂O₃ 821.7756 821.7748 1.0 ppmalpha-Mycolic acid C56:4 C₅₆H₁₀₄O₃ 823.7913 823.7907 0.7 ppmalpha-Mycolic acid C56:3 C₅₆H₁₀₆O₃ 825.8069 825.8053 1.9 ppmalpha-Mycolic acid C56:2 C₅₆H₁₀₈O₃ 827.8226 827.8213 1.6 ppmalpha-Mycolic acid C57:4 C₅₇H₁₀₆O₃ 837.8069 837.8050 2.3 ppmalpha-Mycolic acid C57:3 C₅₇H₁₀₈O₃ 839.8226 839.8215 1.3 ppmalpha-Mycolic acid C58:5 C₅₈H₁₀₆O₃ 849.8069 849.8068 0.1 ppmalpha-Mycolic acid C58:4 C₅₈H₁₀₈O₃ 851.8226 851.8218 0.9 ppmalpha-Mycolic acid C58:3 C₅₈H₁₁₀O₃ 853.8382 853.8375 0.8 ppmalpha-Mycolic acid C59:3 C₅₉H₁₁₂O₃ 867.8539 867.8537 0.2 ppmalpha-Mycolic acid C60:4 C₆₀H₁₁₂O₃ 879.8539 879.8537 0.2 ppmalpha-Mycolic acid C60:3 C₆₀H₁₁₄O₃ 881.8695 881.8683 1.4 ppm

TABLE 6 Table of biomarkers: mycolic acids and their spectrometricsignals Identified mycolic acids as detected in different Mycobacteriumspecies. Exact Sum mass Exp. Mass Compound formula [M − H]− massDeviation alpha-Mycolic acid C77:2 C₇₇H₁₅₀O₃ 1122.1512 1122.1525 1.2 ppmalpha-Mycolic acid C78:2 C₇₈H₁₅₂O₃ 1136.1669 1136.1684 1.3 ppmalpha-Mycolic acid C79:2 C₇₉H₁₅₄O₃ 1150.1825 1150.1833 0.7 ppmEpoxy/keto-Mycolic acid C₇₉H₁₅₄O₄ 1166.1774 1166.1769 0.4 ppm C79:1 orMethoxy- Mycolic acid C79:2 Epoxy/keto-Mycolic acid C₈₀H₁₅₆O₄ 1180.19311180.1897 2.9 ppm C80:1 or Methoxy- Mycolic acid C80:2Epoxy/keto-Mycolic acid C₈₁H₁₅₈O₃ 1194.2087 1194.2102 1.3 ppm C81:1 orMethoxy- Mycolic acid C81:2

TABLE 7 Table of biomarkers: sphingolipids and their spectrometricsignals. Identified sphingolipid species in members of the Bacteroidetesphylum. Experimental Exact Mass Formula mass mass Deviation Observed inCeramide Phosphorylethanolamine/Phosphoethanolamine Dihydroceramides(PE—DHC) C₃₆H₇₄N₂O₇P⁻ 677.5253 677.5239 2.0 B. fragilis, B. ovatus, B.thetaiotaomicron, B. C₃₇H₇₆N₂O₇P⁻ 691.5411 691.5396 2.2 uniformis, B.vulgatus, P. bivia, P. distonasis C₃₈H₇₈N₂O₇P⁻ 705.5569 705.5552 2.4Ceramides C₃₄H₆₉NO₄Cl⁻ 590.4934^(a) 590.4921 2.2 B. fragilis, B. ovatus,B. thetaiotaomicron, B. C₃₅H₇₁NO₄Cl⁻ 604.5090 604.5077 2.1 uniformis, B.vulgatus, P. bivia, P. distonasis C₃₆H₇₃NO₄Cl⁻ 618.5246 618.5234 1.9Bacteroides fragilis α-Galactosylceramides C₄₀H₇₉NO₉Cl⁻ 752.5465752.5449 2.1 B. fragilis C₄₁H₈₁NO₉Cl⁻ 766.5623 766.5605 2.3 C₄₂H₈₃NO₉Cl⁻780.5781 780.5762 2.4 C15:0 substituted Phosphoglycerol Dihydroceramides(subPG—DHC) C₅₀H₁₀₀O₁₀NP 904.7007 904.7028 2.3 B. fragilis, B. ovatus,B. thetaiotaomicron, B. C₅₁H₁₀₂O₁₀NP 918.7163 918.7185 2.4 uniformis, B.vulgatus, P. distonasis C₅₂H₁₀₄O₁₀NP 932.7324^(b) 932.7337 1.4C₅₃H₁₀₆O₁₀NP 946.7481^(b) 946.7484 0.3 C₅₄H₁₀₈O₁₀NP 960.7637^(b)960.7624 1.3 Unsubstituted Phosphoglycerol Dihydroceramides (unPG—DHC)C₃₇H₇₆O₉NP 708.5184 708.5199 2.1 P. distonasis C₃₉H₈₀O₉NP 736.5497736.5484 1.8

TABLE 8 Table of biomarkers: quorum-sensing molecules and theirspectrometric signals Identified quorum-sensing molecules in Psuedomonasaeruginosa. Sum Exp. Mass Compound formula Exact mass mass Deviation2-Heptylquinoline-4(1H)- C₁₆H₂₁NO [M − H]⁻ = 242.1552 −0.8 ppm one242.1550 2-Heptyl-3-hydroxy-4(1H)- C₁₆H₂₁NO₂ [M − H]⁻ = 258.1502 −1.2ppm quinolone (PQS) 258.1499 Hydroxynonenylquinoline C₁₈H₂₃NO [M − H]⁻ =268.1711 −1.5 ppm 268.1707 Hydroxynonylquinoline C₁₈H₂₅NO [M − H]⁻ =270.1868 −1.9 ppm 270.1863 Hydroxyundecenylquinoline C₂₀H₂₆NO [M − H]⁻ =296.2023 −1.0 ppm 296.2020

TABLE 9 Table of biomarkers: Rhamnolipids and their spectrometricsignals. Rhamnolipid species commonly produced by P. aeruginosa strains.Exact Sum mass Exp. Mass Compound formula [M − H]− mass DeviationRha-C₂₀ C₂₆H₄₈O₉ 503.3225 503.3224   0.2 ppm Rha-C_(22:1) C₂₈H₅₀O₉529.3382 529.3384 −0.4 ppm Rha-C₂₂ C₂₈H₅₂O₉ 531.3539 531.3538   0.2 ppmRha-Rha-C₂₀ C₃₂H₅₈O₁₃ 649.3805 649.3804   0.2 ppm Rha-Rha-C₂₂ C₃₄H₆₂O₁₃677.4118 677.4116 −0.3 ppm Rha-Rha-C_(22:1) C₃₄H₆₀O₁₃ 675.3961 675.3965−0.6 ppm

TABLE 10 Table of biomarkers: Surfactins and their spectrometricsignals. Surfactin species detected in positive and negative ion modefor Bacillus subtilis. Negative ion mode Positive ion mode Exact massExact mass Compound Exp. mass [M − H]⁻ Δppm Exp. mass [M + Na]⁺ ΔppmSurfactin(C13) 1006.6453 1006.6440 1.3 1030.6389 1030.6416 2.6Surfactin(C14) 1020.6604 1020.6597 0.7 1044.6545 1044.6573 2.7Surfactin(C15) 1034.6754 1034.6753 0.1 1058.6702 1058.6729 2.6

TABLE 11 Table of biomarkers: Lichenysins and their spectrometricsignals Lichenysin compounds detected in Bacillus licheniformis. Exactmass Compound Exp. mass [M − H]⁻ Δppm Lichenysin (C13) 1005.65941005.6600 0.6 Lichenysin (C14) 1019.6748 1019.6756 0.8 Lichenysin (C15)1033.6906 1033.6913 0.7 Lichenysin (C16) 1047.7055 1047.7070 1.4

TABLE 12 Table of biomarkers Mass spectrometric signals that show strongpositive correlation with the ugcg gene expression for a cell line(NCI60) dataset. Correlation Exp. mass Exact mass Δppm Tentative IDFormula Adduct coefficient 734.5355 734.5343 0.2 GlyCer(d18:1/16:0)C₄₀H₇₇NO₈ [M + Cl]⁻ 0.552 818.6295 818.6282 0.2 GlyCer(d18:1/22:0)C₄₆H₈₉NO₈ [M + Cl]⁻ 0.662 842.6312 842.6332 −0.2 GlyCer(d18:1/24:2)C₄₈H₈₉NO₈ [M + Cl]⁻ 0.602 844.6451 844.6439 0.1 GlyCer(d18:1/24:1)C₄₈H₉₁NO₈ [M + Cl]⁻ 0.668 846.6627 846.6595 0.4 GlyCer(d18:1/24:0)C₄₈H₉₃NO₈ [M + Cl]⁻ 0.688 872.6733 872.6752 −0.2 GlyCer(d18:1/26:1)C₅₀H₉₅NO₈ [M + Cl]⁻ 0.707

TABLE 13 Table of biomarkers for Mycoplasma List of m/z peak that aresignificantly higher in Mycoplasma infected samples compared toMycoplasma free samples in both HEK and HeLa cell lines. Column 2displays the corresponding binned peak, column 2 highlights putativeisotope peaks, while column 4 shows the tentative annotation of thebinned peak. Phosphatidylglycerol and sphingomyelin species, that aremain Mycoplasma constituents are written in bold. significantlydifferent corresponding binned m/z m/z signal Annotation 687.54 687.5468722.51 722.5156 PE(P-36:4) 733.53 733.5231 PE(P-38:4) 747.52 747.5193PG(34:1) 748.53 748.5243 Isotope of irk = 747.52 753.51 753.5090PG(P-36:4) 764.52 764.5264 PE(38:5) 764.53 764.5262 PE(38:5) 766.53766.5412 PE(38:4) 773.54 773.5359 PG(36:2) 774.54 774.5391 PG(36:2),Isotope of m/z = 773.54 774.55 774.5391 PG(36:2), Isotope of m/z =773.54 775.56 775.5520 PG(36:1) 776.56 776.5564 PG(36:1), Isotope of m/z= 775.56 776.57 776.5564 PG(36:1), Isotope of m/z = 775.56 819.52819.5189 PG(40:7) 820.53 820.5268 PG(40:7), Isotope of m/z = 819.52820.54 820.5268 PG(40:7), Isotope of m/z = 819.52

TABLE 14 Table of biomarkers: microbial taxon-specific biomarkersTaxon-specific markers obtained for various microbes. No markers werecalculated where the size of sample set was insufficient. Phylum ClassOrder Family Genus Species No. Gram - Bacteroidetes BacteroidetesBacteroidales Bacteroidaceae Bacteroides Bacteroides acidifaciens 2negative 381.2765 616.5094 576.4764 Bacteroides caccae 2 393.2764617.5124 820.7522 Bacteroides eggerthii 2 590.4923 618.5233 Bacteroidesfragilis 5 591.4963 619.5273 Bacteroides helcogenes 1 592.4883 620.5184Bacteroides ovatus 3 604.5083 627.4883 Bacteroides pyogenes 1 605.5113628.4913 Bacteroides 3 606.5033 635.5004 thetaiotaomicron 616.4724636.5044 Bacteroides uniformis 3 623.5024 637.5044 Bacteroides vulgatus3 624.5054 644.5033 Porphyromonadaceae Parabacteroides Parabacteroidesdistasonis 5 637.5044 648.5003 814.7063 Parabacteroides johnsonii 2639.4954 697.5743 815.7112 640.4993 698.5763 828.7232 653.5113 711.5902829.7262 654.5143 712.5933 840.6842 677.5238 841.6942 691.5395 843.7432705.5562 854.7022 858.6972 872.7072 908.7401 909.7431 910.7471 918.7191921.7912 932.7332 933.7362 934.7422 944.7342 945.7372 946.7472 947.7502948.7562 949.7592 958.7461 959.7501 960.7611 961.7661 962.7691Prevotellaceae Prevotella Prevotella bivia 7 661.5283 675.5453 676.5503870.8002 908.7401 922.7552 923.7612 953.5113 Rikenellaceae AlistipesAlistipes onderdonkii 1 Flavobacteria Flavobacteriales FlavobacteriaceaeChryseobacterium Chryseobacterium 3 324.2545 indologenes 333.2084Chryseobacterium sp 1 390.2324 Elizabethkingia Elizabethkingia 4392.2484 meningoseptica 393.2504 Myroides Myroides odoratimimus 2552.4643 553.4674 553.4674 554.4714 556.4034 565.4654 566.4794 567.4834568.4864 600.4664 601.4723 618.4773 619.4813 620.4883 651.4953 651.4953891.7411 Fusobacteria Fusobacteria Fusobacteriales FusobacteriaceaeFusobacterium Fusobacterium gonidiaformans 3 227.2015 Fusobacteriumnecrophorum 7 644.4652 Fusobacterium peridontiam 4 645.4633Fusobacterium sp 1 646.4833 647.4812 648.4832 673.4443 696.4953 714.5492856.6782 865.6632 884.7083 Proteobacteria Alpha- CaulobacteralesCaulobacteraceae Brevundimonas Brevundimonas diminuta 2 768.5182Proteobacteria 769.5502 782.5342 770.5562 783.5293 771.5582 795.5572797.5723 818.5673 957.6261 Rhizobiales Rhizobiaceae Rhizobium Rhizobiumradiobacter 5 439.4155 440.4195 739.5313 784.5902 785.5932 799.5132Rhodospirillales Acetobacteraceae Roseomonas Roseomonas mucosa 6662.5393 Roseomonas sp 1 722.5753 729.5813 733.5752 733.6173 734.5753747.6283 757.6173 Beta- Burkholderiales Alcaligenaceae AchromobacterAchromobacter sp 3 Proteobacteria Achromobacter 3 xylosoxidansAlcaligenes Alcaligenes faecalis 3 Burkholderiaceae BurkholderiaBurkholderia cepacia 7 589.4013 complex 590.4083 591.4184 592.4214Comamonadaceae Acidovorax Acidovorax temperans 2 520.3044 ComamonasComamonas kerstersii 2 Comamonas sp 1 Delftia Delftia acidovorans 4Delftia dentocariosa 1 Delftia sp 2 Sutterellaceae Sutterella Sutterella2 wadsworthensis Neisseriales Neisseriaceae Eikenella Eikenellacorrodens 1 494.3855 Kingella Kingella kingae 3 502.3674 Kingella sp 1526.3673 Neisseria Neisseria cineria 1 527.3704 Neisseria elongata 2528.3653 Neisseria flavescens 3 544.3774 Neisseria gonorrhoea 4Neisseria lactamica 3 Neisseria meningitidis 4 Neisseria mucosa 2Epsilon- Campylobacterales Campylobacteraceae CampylobacterCampylobacter coli 1 Proteobacteria 867.6582 Campylobacter fetus 3730.5422 993.8381 Campylobacter jejuni 3 731.5452 Campylobacter sp 6867.6582 Helicobacteraceae Helicobacter Helicobacter pylori 3 993.8381271.2284 272.2305 299.2595 300.2625 400.2644 543.4623 544.4634 Gamma-Aeromonadales Aeromonadaceae Aeromonas Aeromonas hydrophila 1Proteobacteria Cardiobacteriales Cardiobacteriaceae CardiobacteriumCardiobacterium hominis 4 648.4603 649.4623 650.4653 793.4792 794.4802Enterobacteriales Enterobacteriaceae Citrobacter Citrobacteramalonaticus 1 702.5083 Citrobacter braakii 3 703.5092 Citrobacterfreundii 4 993.7282 Citrobacter koseri 4 994.7272 EnterobacterEnterobacter absuriae 2 Enterobacter aerogenes 3 Enterobacter amnigenus1 Enterobacter cloacae 3 Enterobacter gergoviae 1 EscherichiaEscherichia coli 7 Hafnia Hafnia alvei 3 Hafnia paralvei 2 Hafnia sp 1Klebsiella Klebsiella oxytoca 5 Klebsiella pneumoniae 5 MorganellaMorganella morganii 7 Panthoea Panthoea sp 1 Proteus Proteus mirabilis 5Proteus vulgaris 5 Provedencia Provedencia rettgeri 2 Provedenciastuartii 2 Raoultella Raoultella ornithololytica 1 Raoultella planticola1 Salmonella Salmonella poona 1 Serratia Serratia liquifaciens 3Serratia marcescens 5 Shigella Shigella sonnei 1 PasteurellalesPasteurellaceae Aggregatibacter Aggregatibacter 5 690.4983 aphrophilus746.4503 Haemophilus Haemophilus influenzae 5 823.5453 Haemophilus 2898.6921 parahaemolyticus 915.6902 Haemophilus 1 977.7282 parainfluenzaePasteurella Pasteurella multocida 2 Pseudomonadales MoraxellaceaeAcinetobacter Acinetobacter baumanii 5 Acinetobacter iwoffii 5Acinetobacter johnsonii 2 Acinetobacter junii 1 Moraxella Moraxellacatarrhalis 5 Moraxella osloensis 2 Pseudomonadaceae PseudomonasPseudomonas 7 286.1805 aearuginosa 490.3304 Pseudomonas luteola 1514.3294 Pseudomonas monteilii 2 Pseudomonas 2 oryzihabitans Pseudomonasputida 1 Pseudomonas stutzeri 5 Vibrionales Vibrionaceae Vibrio Vibrioalginolyticus 1 605.3823 Vibrio cholerae 1 607.3983 Vibrio furnissii 1608.4013 633.4134 Xanthomonadales Xanthomonadaceae StenotrophomonasStenotrophomonas 7 377.2105 maltophilia 562.3504 619.4353 620.4384705.4713 706.4743 929.6852 930.6892 942.6912 943.7012 944.7052 Gram -Actinobacteria Actinobacteria Actinomycetales ActinomycetaceaeActinobaculum Actinobaculum schaalii 2 positive 757.5403 ActinomycesActinomyces graevenitzii 1 879.6112 Actinomyces israelii 1 Actinomyces 2odontolyticus Actinomyces oris 5 Actinomyces sp 1 Actinomyces turicensis1 Actinomyces viscosis 2 Corynebacteriaceae CorynebacteriumCorynebacterium 2 493.4624 afermentans 495.4784 Corynebacterium 3497.4845 amycolatum 521.4934 Corynebacterium 2 535.4734 diphtheriae537.4904 Corynebacterium imitans 3 538.4934 Corynebacterium 1minutissimum Corynebacterium sp 5 Corynebacterium 3 striatumMicrobacteriaceae Microbacterium Microbacterium sp 1 MycobacteriaceaeMycobacterium Mycobacterium avium 2 391.3684 Mycobacterium fortuitum 1427.0965 Mycobacterium 1 724.8873 peregrium 817.4152 850.5592 851.5662852.5672 Nocardiaceae Nocardia Nocardia sp 1 321.2915 RhodococcusRhodococcus equi 1 743.7273 Rhodococcus sp 2 771.7592 797.7762 798.7762800.7962 827.8162 828.8222 970.7871 PropionibacteriaceaePropionibacterium Propionibacterium acnes 7 361.2155 617.4564 713.4752714.4812 779.5072 877.5592 906.5872 Bifidobacteriales BifidobacteriaceaeBifidobacterium Bifidobacterium 1 789.5293 adolescentis 792.5502Bifidobacterium bifidum 2 819.5783 Bifidobacterium breve 3 830.5622Bifidobacterium infantis 1 855.5272 Bifidobacterium longum 3 884.6092Bifidobacterium 2 885.6142 pseudocatenulatum Gardnerella Gardnerellavaginalis 2 Micrococcales Micrococcaceae Arthrobacter Arthrobacter 1913.5682 913.5682 creatinolyticus 914.5711 Arthrobacter sp 1 915.5671Kokuria Kokuria kristina 2 Kokuria rhizophila 2 Kokuria varians 1Micrococcus Micrococcus luteus 5 Micrococcus lylae 2 Rothia Rothia aeria3 Rothia amarne 1 Rothia dentocariosa 5 Rothia mucilaginosa 5 Rothia sp1 Micrococcineae Brevibacterium Brevibacterium 1 paucivoransBrevibacterium sp 3 Dermabacter Dermabacter hominis 2 Dermobacter sp 1Firmicutes Bacilli Bacillales Bacillaceae Bacillus Bacillus cereus 3Bacillus clausii 3 Bacillus lichenformis 3 Bacillus pumilus 1 Bacillussonorensis 1 Bacillus sp 3 Bacillus subtilis 3 Listeriaceae ListeriaListeria monocytogenes 7 675.9793 832.5352 PaenibacillaceaePaenibacillus Paenibacillus sp 5 871.5892 Paenibacillus unalis 1903.7221 914.7282 915.7282 916.7282 Staphylococcaceae StaphylococcusStaphylococcus aureus 3 763.5512 Staphylococcus capitis 3 765.5482Staphylococcus caprae 1 Staphylococcus cohnii 4 Staphylococcus epidermis3 Staphylococcus 3 haemolyticus Staphylococcus hominis 3 Staphylococcus3 lugdunensis Staphylococcus pasteuri 3 Staphylococcus 3 pettenkoferiStaphylococcus 3 saprophyticus Staphylococcus warneri 3 LactobacillalesAerococcaceae Abiotrophia Abiotrophia defectiva 1 898.5391 163.0506923.5512 925.5671 926.5701 928.5952 949.5672 950.5692 951.5832 952.5861953.5981 954.6011 955.5971 956.5971 979.6111 990.6001 AerococcusAerococcus sp 1 Aerococcus viridans 2 Carnobacteriaceae GranulicatellaGranulicatella adiacens 1 Enterococcaceae Enterococcus Enterococcusavium 3 Enterococcus 2 casseliflavus Enterococcus cecorum 1 Enterococcusfaecalis 3 Enterococcus faecium 3 Enterococcus gallinarum 3 Enterococcusraffinosus 3 Lactobacillaceae Lactococcus Lactococcus lactis 1Lactococcus spp 2 Leuconostocaceae Leuconostoc Leuconostoc sp 1Streptococcaceae Lactobacillus Lactobacillus gasseri 2 897.5351Lactobacillus rhamnosus 3 Streptococcus Streptococcus agalactiae 3Streptococcus anginosus 3 Streptococcus bovis 3 Streptococcus canis 1Streptococcus 2 constellatus Streptococcus cristatus 2 Streptococcus 3dysagalactiae Streptococcus gallolyticus 3 Streptococcus gordonii 3Streptococcus 3 intermedius Streptococcus lutetiensis 3 Streptococcusmilleri 3 Streptococcus mitis 3 Streptococcus mutans 3 Streptococcusoralis 3 Streptococcus 3 parasanguinus Streptococcus 3 pneumoniaeStreptococcus povas 1 Streptococcus 2 pseudoporcinus Streptococcuspyogenes 3 Streptococcus salivarius 3 Streptococcus sanguinis 3Streptococcus vestibularis 1 Streptococcus viridans 3 ClostridiaClostridiales Clostridiaceae Clostridium Clostridium 1 449.2685 649.4453celerecrescens 703.4923 731.5253 Clostridium difficile 4 704.4953897.6951 Clostridium histolyticum 2 731.5253 925.7262 Clostridiuminnocuum 3 732.5283 969.7481 Clostridium 2 925.7262 970.7541paraputrificum Clostridium perfringens 3 Clostridium ramosum 3Clostridium septicum 2 Clostridium sporogenes 2 Clostridium tertium 3Peptostreptococcaceae Parvinomas Parvinomas micra 1 496.4124Peptoniphilus Peptoniphilus harei 5 497.4214 498.4244 635.3944 645.4133646.4173 681.3923 Negativicutes Selenomonadales AcidaminococcaceaeAcidaminococcus Acidaminococcus 2 423.3505 627.4403 fermentans 425.3644643.4343 426.3674 644.4383 461.3394 730.4652 560.4194 734.5933 851.7352831.5902 977.6971 978.6931 Veillonellaceae Dialister Dialister sp 1218.1855 Veillonella Veillonella atypica 1 229.1815 Veillonella dispar 1358.2145 Veillonella parvula 1 364.2495 Veillonella ratti 1 655.4713

TABLE 16 Taxon-specific markers as determined on phylum-level.Phylogenetic information Taxonomic level m/z value Compound IDGram-negatives Bacteroidetes 381.2765 spingolipid (Phylum) 653.5113Isotope m/z = 653 654.5143 CerP(d34:1)) 623.5024 isotope m/z = 623640.4993 isotope m/z = 635 639.4954 isotope m/z = 590 393.2764Cer(d18:0/h17:0) 616.4724 isotope m/z = 604 624.5054 isotope m/z = 604637.5044 Cer(d34:0(2OH) 592.4883 isotope m/z = 590 604.5083 PE—DHC605.5113 PE—DHC 606.5033 PE—DHC 590.4923 591.4963 705.5562 691.5395677.5238 Fusobacteria 646.4833 PE plasmalogen (Phylum) 227.2015 PEplasmalogen 648.4832 combinatorial marker 856.6782 with m/z = 227865.6632 696.4953 714.5492 673.4443 644.4652 884.7083 645.4633 647.4812Proteobacteria 768.5182 782.5342 783.5293 Gram-positives Actinobacteria— Firmicutes —

TABLE 17 Taxon-specific markers as determined on class-level.Phylogenetic information Taxonomic level m/z value Compound IDGram-negatives Bacteroidetes 635.5004 sphingolipid ^(└)Bacteroidetes616.5094 Cer(d36:1(2OH)) 628.4913 PE-Cer(33:1) 636.5044 CerP(d36:1)627.4883 Cer(d36:0(2OH)) 644.5033 isotope 618 711.5902 DG(42:5) 618.5233isotope m/z = 616 712.5933 619.5273 697.5743 620.5184 698.5763 648.5003637.5044 617.5124 Flavobacteria 333.2084 390.2324 566.4794 567.4834568.4864 556.4034 600.4664 565.4654 553.4674 392.2484 651.4953 618.4773619.4813 324.2545 620.4883 393.2504 891.7411 554.4714 552.4643 553.4674651.4953 601.4723 Gram-negatives Fusobacteria (class) ^(└)FusobacteriaGram-negatives Alpha-Proteobacteria ^(└)ProteobacteriaBeta-Proteobacteria — Epsilon-Proteobacteria 993.8381 867.6582 731.5452730.5422 Gamma-Proteobacteria — Gram-positives Actinobacteria (class) —^(└)Actinobacteria Gram-positives Bacilli — ^(└)Firmicutes Clostridia731.5253 PG plasmalogen 732.5283 Isotope m/z = 731 449.2685 PGplasmalogen 703.4923 Isotope m/z = 703 925.7262 704.4953 Negativicutes560.4194 Isotope m/z = 425 426.3674 425.3644 423.3505 461.3394 851.7352

TABLE 18 Taxon-specific markers as determined on order-level.Phylogenetic information Taxonomic level m/z value Compound IDGram-negatives Bacteroidales ^(└)Bacteroidetes ^(└)BacteroidetesGram-negatives Flavobacteriales ^(└)Bacteroidetes ^(└)FlavobacteriaGram-negatives Fusobacteriales ^(└)Fusobacteria ^(└)FusobacteriaGram-negatives Caulobacterales 795.5572 ^(└)Proteobacteria 797.5723^(└)Alpha-Proteobacteria 769.5502 770.5562 957.6261 771.5582 818.5673Rhizobiales 739.5313 Isotope m/z = 784.5902 784 785.5932 Isotope m/z =439.4155 439 440.4195 799.5/32 Rhodospiralles 733.5752 734.5753 729.5813733.6173 722.5753 662.5393 747.6283 757.6173 Gram-negativesBurkholderiales — ^(└)Proteobacteria Neisseriales 526.3673 Isotope m/z =^(└)Beta-Proteobacteria 527.3704 526 502.3674 544.3774 494.3855 528.3653Gram-negatives Campylobacterales — ^(└)Proteobacteria^(└)Epsilon-Proteobacteria Gram-negatives Aeromonadales^(└)Proteobacteria Cardiobacterales 648.4603 Isotope m/z =^(└)Gamma-Proteobacteria 649.4623 648 793.4792 650.4653 794.4802Enterobacteriales 703.5092 Isotope m/z = 702.5083 702 993.7282 994.7272Pasteurellales 746.4503 915.6902 823.5453 898.6921 690.4983 977.7282Pseudomonadales — Vibrionales 607.3983 Isotope m/z = 608.4013 607633.4134 605.3823 Xanthomonadales 562.3504 Isotope m/z = 377.2105 619619.4353 Isotope m/z = 620.4384 629 930.6892 Isotope m/z = 929.6852 643944.7052 Isotope m/z = 943.7012 705 942.6912 PG(31:1) 706.4743 705.4713Gram-positives Actinomycetales — ^(└)Actinobacteria Bifidobacteriales792.5502 ^(└)Actinobacteria 819.5783 884.6092 885.6142 789.5293 830.5622855.5272 Micrococcales 913.5682 Gram-positives Bacillales ^(└)FirmicutesLactobacillales 951.5832 ^(└)Bacilli 954.6011 952.5861 953.5981 925.5671956.5971 955.5971 926.5701 950.5692 949.5672 928.5952 990.6001 923.5512898.5391 979.6111 Clostridiales Selemonadales

TABLE 19 Taxon-specific markers as determined on family-levelPhylogenetic m/z information Taxonomic level value Compound IDGram-negatives Bacteroidaceae 820.7522 ^(└)Bacteroidetes Porphyro-841.6942 isotope m/z = 840 ^(└)Bacteroidetes monadaceae 840.6842 isotopem/z = 946 ^(└)Bacteroidales 948.7562 isotope m/z = 946 949.7592 isotopem/z = 946 947.7502 SubPG DHC 946.7472 isotope m/z = 944 945.7372 SubPGDHC 944.7342 isotope m/z = 932 933.7362 SubPG DHC 932.7332 isotope m/z =814 872.7072 isotope m/z = 960 815.7112 SubPG DHC 814.7063 isotope m/z =960 858.6972 isotope m/z = 828 934.7422 isotope m/z = 958 962.7691960.7611 961.7661 828.7232 829.7262 854.7022 959.7501 958.7461 921.7912918.7191 843.7432 910.7471 908.7401 909.7431 Prevotellaceae 661.5283908.7401 675.5453 922.7552 923.7612 676.5503 870.8002 RikenellaceaeGram-negatives Flavo- ^(└)Bacteroidetes bacteriaceae ^(└)Flavobacteria^(└)Flavobacteriales Gram-negatives Fusobacteriaceae ^(└)Fusobacteria^(└)Fusobacteria ^(└)Fusobacteriales Gram-negatives Caulobacteraceae^(└)Proteobacteria ^(└)Alpha-Proteobacteria ^(└)CaulobacteralesGram-negatives Rhizobiaceae ^(└)Proteobacteria ^(└)Alpha-Proteobacteria^(└)Rhizobiales Gram-negatives Acetobacteraceae ^(└)Proteobacteria^(└)Alpha-Proteobacteria ^(└)Rhodospiralles Gram-negativesAlcaligenaceae — ^(└)Proteobacteria Burkholderiaceae 589.4013 Isotopem/z = 589 ^(└)Beta-Proteobacteria 591.4184 Isotope m/z = 591^(└)Burkholderiales 590.4083 592.4214 Comamonadaceae 520.3044Sutterellaceae — Gram-negatives Neisseriaceae ^(└)Proteobacteria^(└)Beta-Proteobacteria ^(└)Neisseriales Gram-negatives Campylo-993.8381 ^(└)Proteobacteria bacteraceae 867.6582 ^(└)Epsilon- Helico-299.2595 C18:0(+O) Proteobacteria bacteriaceae 300.2625 Isotope m/z =299 ^(└)Campylobacterales 272.2305 Isotope m/z = 271 271.2284 C16:0(+O)543.4623 400.2644 544.4634 Gram-negatives Cardio- ^(└)Proteobacteriabacteriaceae ^(└)Gamma- Proteobacteria ^(└)CardiobacteralesGram-negatives Entero- ^(└)Proteobacteria bacteriaceae ^(└)Gamma-Proteobacteria ^(└)Enterobacterales Gram-negatives Pasteurellaceae^(└)Proteobacteria ^(└)Gamma- Proteobacteria ^(└)PasteurellalesGram-negatives Moraxellaceae — ^(└)Proteobacteria Pseudo- 514.3294^(└)Gamma- monadaceae 490.3304 Proteobacteria 286.1805^(└)Pseudomonadales Gram-negatives Vibrionaceae ^(└)Proteobacteria^(└)Gamma- Proteobacteria ^(└)Vibrionales Gram-negatives Xantho-^(└)Proteobacteria monadaceae ^(└)Gamma- Proteobacteria^(└)Xanthomonadales Gram-positives Actinomyceteae 757.5403 Combinatorial^(└)Actinobacteria 879.6112 markers ^(└)Actinobacteria Coryne- 537.4904Mycolic acid C35:0 ^(└)Actinomycetales bacteriaceae 538.4934 Isotope m/z= 537 535.4734 Mycolic acid C35:1 493.4624 Mycolic acid C32:1 495.4784Mycolic acid C32:0 497.4845 Isotope m/z = 495 521.4934 Mycolic acidC34:1 Microbacteriaceae Mycobacteriaceae 851.5662 PI(35:0) 852.5672Isotope m/z = 851 850.5592 391.3684 724.8873 427.0965 817.4152Nocardiaceae 798.7762 Isotope m/z = 797 797.7762 Mycolic acid C54:3828.8222 Isotope m/z = 827 970.7871 combinatorial 321.2915 Mycolic acidC56:2 827.8162 Isotope Mycolic 800.7962 acid C54:2 743.7273 Mycolic acidC50:2 771.7592 Mycolic acid C52:2 Propioni- 617.4564 bacteriaceae906.5872 779.5072 714.4812 361.2155 713.4752 877.5592 Gram-positivesBifido- 792.5502 ^(└)Actinobacteria bacteriaceae 819.5783^(└)Actinobacteria ^(└)Bifidobacteriales Gram-positives Micrococcaceae913.5682 Isotope m/z = 913 ^(└)Actinobacteria 914.5711^(└)Actinobacteria 915.5671 ^(└)Micrococcales MicrococcineaeGram-positives Bacillaceae ^(└)Firmicutes Listeriaceae 675.9793^(└)Bacilli 832.5352 └Bacillales Paenibacillaceae 915.7282 916.7282914.7282 871.5892 903.7221 Staphylo- 765.5482 Isotope m/z = 763coccaceae 763.5512 PG(35:0) Gram-positives Aerococcaceae 163.0506^(└)Firmicutes Carnobacteriaceae ^(└)Bacilli Enterococcaceae —^(└)Lactoacillales Lactobacillaceae — Leuconostocaceae Streptococcaceae897.5351 Gram-positives Clostridiaceae 731.5253 ^(└)Firmicutes 970.7541^(└)Clostridia 649.4453 ^(└)Clostridiales 897.6951 969.7481 925.7262Peptostrepto- 497.4214 Isotope m/z = 497 coccaceae 498.4244 Isotope m/z= 645 681.3923 635.3944 496.4124 645.4133 646.4173 Gram-positivesAcidamino- 730.4652 ^(└)Firmicutes coccaceae 627.4403 ^(└)Negativicutes831.5902 ^(└)Selemonadales 977.6971 978.6931 643.4343 644.4383 734.5933Veillonellaceae 229.1815 218.1855 364.2495 655.4713 358.2145

TABLE 20 m/z IDs CD ANOVA pVal ANOVA qVal Healthy EC (Mean) HO (Mean) SC(Mean) SA (Mean) MedFC-HO-SC MeanFC-HO-SA 756.5955 PE(P- SC 0.03335362 1       0 0.001 2.9186 0.6746 11.51106078 9.397888508 38:1) 865.5746PI(36:0) SC 8.99775E−06 0.000181998 4.2331 0.2857 16.469 3.33485.849108111 3.545027299 747.4995 PA(40:6) SC 0.000029587 0.000487705 00.8051 33.1513 23.0009 5.363753646 4.836378514 882.5255 PS(44:10) SC2.09342E−06 4.83105E−05 1.2377 0.7999 17.5562 3.0372 4.4560171481.924850356 729.5466 PA(38:1) SC 0.000187847 0.00232043  0 0.6001 7.38362.2515 3.621049563 1.907611643 836.5385 PS(40:5) SC 0.0002277570.002766326 11.8159 4.1195 50.29 12.0226 3.609730406 1.545207779907.5386 PI(40:7) SC 0.001565923 0.01540735  0 0.2976 3.3043 0.56943.472898245 0.936067967 721.5045 PG(32:0) SC  2.591E−07 7.14918E−066.4138 1.2772 13.6359 2.3595 3.416353561 0.885496713 725.5165 PA(38:3)SC 0.001014647 0.01044902  8.9208 5.875 53.9985 45.8083 3.2002585752.962948267 890.5915 PS(44:6) SC 8.93408E−05 0.001229222 0.7119 1.155410.2085 1.4504 3.143306593 0.32805843 889.5745 TG(P- SC 2.89892E−077.87048E−06 5.4933 5.5739 48.3826 17.6337 3.117729275 1.66157619658:20)/ PI(38:2) 720.5005 PE(P- SC  3.9936E−05 0.000627725 9.821 3.401427.6954 6.804 3.025445795 1.000254466 36:5) 798.6055 RE(40:2) SC4.95954E−07 1.28467E−05 0 0.9016 7.193 1.6266 2.996034183 0.851300098864.5816 PS(42:5) SC 8.48139E−07 2.07019E−05 62.7448 11.6176 91.878928.7887 2.983421521 1.30919058 816.5585 PE(42:7) SC 1.52034E−107.71875E−09 0 1.6337 12.8847 4.1834 2.979443959 1.356532867 881.5234PI(38:6) SC 3.06222E−07 8.22587E−06 20.1924 5.8436 44.2926 10.58512.922136354 0.857105566 909.5536 PI(40:6) SC 2.24965E−12 1.70469E−1049.0519 15.9328 114.7333 36.869 2.848212447 1.210408461 762.5125PE(38:6) SC 8.97872E−05 0.001232026 52.1501 10.1699 70.944 40.85212.802375182 2.006104749 796.5915 PE(40:3) SC 3.79122E−06 8.22564E−055.1935 4.9805 33.6477 20.3448 2.756145403 2.030297609 818.5755 PE(42:6)SC 0.000301686 0.003521058 0.3887 2.2777 14.9853 7.765 2.7178983221.769408185 688.4956 PE(32:1) SC 0.004342958 0.03875079  0 1.5169 9.76151.7478 2.685976876 0.204414127 698.5165 PE(P- SC 2.68247E−05 0.00044361315.976 10.0868 60.439 12.0642 2.583011233 0.258263694 34:2) 730.5425PE(35:1) SC 9.22832E−07 2.22048E−05 3.0568 3.4431 18.7517 6.16622.445241406 0.840673601 863.5705 PI(36:1) SC  7.6787E−07 1.89247E−05176.9816 37.4607 200.7699 75.9336 2.422093229 1.019360549 671.4685PA(34:2) SC 0.005222022 0.04586887  1.5406 1.0287 5.4109 2.22092.395046269 1.110322124 860.5435 PS(42:7) SC 1.01514E−08 3.60408E−0711.4123 9.893 48.7253 18.1579 2.300191086 0.876117379 862.5576 PS(42:6)SC 2.93214E−09 1.20052E−07 65.6885 24.024 111.5443 49.95 2.2150685081.056008298 888.5745 PS(44:7) SC 2.70063E−09 1.12386E−07 135.885764.5795 280.929 113.4454 2.121057384 0.812849935 859.5395 PI(36:3) SC1.31586E−08 4.51393E−07 77.7761 26.7501 109.2902 47.0364 2.0305478510.814233361 752.5645 PE(P- SC 0.000105486 0.001401967 9.7931 17.582470.3579 40.0642 2.000580411 1.188181657 38:3) 699.5004 PA(36:2) SC2.16751E−06 4.93473E−05 21.1742 21.3245 83.9542 57.5307 1.9770905861.431820109 697.4845 PA(36:3) SC 0.003052866 0.02802785  2.6456 1.96777.6299 2.813 1.955153868 0.515599272 807.5075 PI(32:1) SC 0.0015283 0.01506637  1.2143 3.5478 13.544 2.8181 1.93265729 −0.332201876 724.5235PE(P- SC 3.34117E−08 1.07361E−06 13.3679 24.4632 90.6672 36.24251.889967599 0.567069342 36:3) 728.5635 PE(P- SC 5.35704E−08  1.6893E−0675.1233 24.7786 90.3378 34.6904 1.866235103 0.485441799 36:1) 772.5896PE(38:1) SC 5.81536E−05 0.001008793 79.1078 42.7078 147.5674 109.72661.788802554 1.36134182 861.5535 PI(36:2) SC 1.11286E−08 3.86985E−07116.1198 72.7123 249.9238 138.848 1.781216958 0.93323506 788.5254PE(40:7) SC 2.13379E−06 4.87984E−05 19.5351 17.2225 52.4945 26.02421.607871697 0.595559237 820.5906 PE(42:5) SC 0.000846836 0.008846474 04.7141 13.8708 7.9687 1.55699673 0.757362022 726.5476 PE(P- SC2.57574E−07 7.14592E−06 85.965 33.5187 95.8155 34.6192 1.5152928620.04660619 36:2) 770.5735 PE(38:2) SA 2.30565E−06 5.20258E−05 152.3456128.0562 325.133 327.1652 1.344252888 1.353242195 690.5105 PE(32:0) SC0.004089558 0.03681327  1.8308 9.2329 23.0854 13.957 1.3221249640.596133107 740.5284 PE(36:3) SC 9.15878E−07 2.21424E−05 56.0117 58.7099143.5809 71.9384 1.290188141 0.293158273 768.5585 PE(38:3) SC1.58943E−05 0.000287172 191.6569 129.141 290.4791 253.9433 1.1694872620.975559307 911.5704 PI(40:5) SC 7.21238E−08 2.24646E−06 52.6973 40.899385.8625 42.5665 1.069952028 0.057642319 723.4995 PA(38:4) SA 0.00037395 0.004275998 7.4888 20.0312 41.4992 62.2382 1.050834675 1.635551486742.5424 PE(36:2) SC 2.87707E−06 6.29608E−05 395.1418 345.5038 692.7696581.6404 1.003674045 0.751425902 701.5155 PA(36:1) SA  1.406E−050.000272453 104.0595 173.6574 343.5916 393.7826 0.984450877 1.181155475714.5105 PE(34:2) SC 2.38041E−06  5.3475E−05 21.3764 38.1989 75.304429.4042 0.979203069 −0.377508854 744.5575 PE(36:1) SC 0.0004342160.004834464 782.4336 603.4562 1019.6619 1009.0828 0.7567698960.741723593 872.6425 PS(42:1) SC 0.001341361 0.01337935  2.6642 9.75616.3726 4.596 0.746921779 −1.08591096 746.5755 PE(36:0) SC 0.0004078380.004601323 37.397 47.869 79.349 55.9679 0.729120374 0.225507949819.5536 PG(P- HO 3.34792E−05 0.000543048 41.2026 48.6631 23.576717.1004 −1.045466427 −1.508798156 41:6) 816.5805 PE(38:2)/ HO1.26476E−08 4.36814E−07 71.9373 73.9657 35.4711 22.4157 −1.060212336−1.722346854 PS(38:1) 788.5475 PS(36:1) HO 1.24345E−14 1.34319E−121310.7695 1946.6457 887.8471 1068.1457 −1.132607179 −0.865881879749.5355 PG(34:0) HO 2.26439E−11 1.40199E−09 246.3929 344.2116 150.2673151.2288 −1.195764622 −1.186562805 748.5325 PE(P- HO 3.12647E−111.84571E−09 511.038 745.9556 301.8235 364.2202 −1.305384624 −1.03427882538:5) 868.6124 PS(42:3) HO 0.000341653 0.003960214 0 4.6648 1.713 0.4246−1.445290076 −3.457638952 814.5655 PE(38:3)/ HO 3.33067E−16 4.57022E−1445.6966 106.7905 25.2074 25.4809 −2.082864087 −2.067295171 PS(38:2)847.5665 PI(P- HO 1.09395E−07 3.30593E−06 6.2006 13.3224 3.044 2.4174−2.12981374 −2.462325888 36:1) 846.5635 PS(P- HO 5.18541E−12 3.60634E−1024.6104 30.5856 4.4186 7.6053 −2.791191338 −2.007775515 42:6) 724.5325PE(P- HO 9.14935E−13 7.25801E−11 32.8176 65.1551 7.4891 8.0302−3.121013851 −3.020370285 36:3) 818.5316 PS(P- HO 2.22045E−163.22092E−14 37.1244 38.8126 4.0168 5.1328 −3.272406545 −2.91870712840.6) SC = Serous carcinoma; HO = Healthy ovary; SA = StromaA; CD =Class Diff Number of lipids Class Diff class where the p value issignificant PA 4 HealthyEC (Mean) mean intensity of epithelial cellsfrom Fallopian tube PE 14  HealthyOv (Mean) mean intensity of healthystroma PI 4 SerousCarcinoma (Mean) mean intensity of cancer cells fromSerous adenocarcinomas PS 9 StromaA (Mean) mean intensity of cancerassociated stroma PG 3 MeanFC-HealthyOv-SerousCarcinoma fold change ofmean - log(SerousCarcinoma/HealthyOv) MeanFC-HealthyOv-StromaA foldchange of mean - log(StromaA/HealthyOv)

Although the present invention has been described with reference topreferred embodiments, it will be understood by those skilled in the artthat various changes in form and detail may be made without departingfrom the scope of the invention as set forth in the accompanying claims.

The invention claimed is:
 1. A method of analysis using massspectrometry and/or ion mobility spectrometry comprising: (a) using afirst device to generate smoke, aerosol or vapour from a target in vitroor ex vivo cell population and/or culture medium derived therefrom; (b)adding a matrix to said aerosol, smoke or vapour, wherein said matrixcomprises isopropanol; (c) mass analysing and/or ion mobility analysingsaid smoke, aerosol or vapour, or ions derived therefrom, in order toobtain spectrometric data; and (d) analysing said spectrometric data inorder to identify and/or characterise said target in vitro or ex vivocell population or one or more cells and/or compounds present in saidtarget in vitro or ex vivo cell population and/or culture medium derivedtherefrom, wherein the step of analysing comprises analysing saidspectrometric data in order to: analyse whether there is an infection ofsaid in vitro or ex vivo cell population; or analyse the genotype and/orphenotype of said in vitro or ex vivo cell population or one or morecell types present therein; or analyse the effect of manipulating thegenotype and/or phenotype of said in vitro or ex vivo cell population orone or more cell types present therein.
 2. A method as claimed in claim1, further comprising causing said aerosol, smoke or vapour, or analytetherein, to impact upon a collision surface located within a vacuumchamber of a spectrometer so as to generate a plurality of analyte ions.3. A method as claimed in claim 2, comprising the step of transferringsaid analyte ions to an analysis region and then mass analysing and/orion mobility analysing said smoke, aerosol or vapour, or ions derivedtherefrom, in order to obtain spectrometric data.
 4. A method as claimedin claim 1, wherein said step of using said first device to generateaerosol, smoke or vapour from one or more regions of the target furthercomprises irradiating said target with a laser.
 5. A method as claimedin claim 1, wherein said first device comprises or forms part of an ionsource selected from the group consisting of: (i) a rapid evaporativeionisation mass spectrometry (“REIMS”) ion source; (ii) a desorptionelectrospray ionisation (“DESI”) ion source; (iii) a laser desorptionionisation (“LDI”) ion source; (iv) a thermal desorption ion source; (v)a laser diode thermal desorption (“LDTD”) ion source; (vi) a desorptionelectro-flow focusing (“DEFFI”) ion source; (vii) a dielectric barrierdischarge (“DBD”) plasma ion source; (viii) an Atmospheric SolidsAnalysis Probe (“ASAP”) ion source; (ix) an ultrasonic assisted sprayionisation ion source; (x) an easy ambient sonic-spray ionisation(“EASI”) ion source; (xi) a desorption atmospheric pressurephotoionisation (“DAPPI”) ion source; (xii) a paperspray (“PS”) ionsource; (xiii) a jet desorption ionisation (“JeDI”) ion source; (xiv) atouch spray (“TS”) ion source; (xv) a nano-DESI ion source; (xvi) alaser ablation electrospray (“LAESI”) ion source; (xvii) a directanalysis in real time (“DART”) ion source; (xviii) a probe electrosprayionisation (“PESI”) ion source; (xix) a solid-probe assistedelectrospray ionisation (“SPA-ESI”) ion source; (xx) a cavitronultrasonic surgical aspirator (“CUSA”) device; (xxi) a hybridCUSA-diathermy device; (xxii) a focussed or unfocussed ultrasonicablation device; (xxiii) a hybrid focussed or unfocussed ultrasonicablation and diathermy device; (xxiv) a microwave resonance device;(xxv) a pulsed plasma RF dissection device; (xxvi) an argon plasmacoagulation device; (xxvii) a hybrid pulsed plasma RF dissection andargon plasma coagulation device; (xxviii) a hybrid pulsed plasma RFdissection and JeDI device; (xxix) a surgical water/saline jet device;(xxx) a hybrid electrosurgery and argon plasma coagulation device; and(xxxi) a hybrid argon plasma coagulation and water/saline jet device. 6.A method as claimed in claim 1, wherein said cell population isidentified, confirmed or authenticated as comprising or consisting ofmutant and/or transgenic cells on the basis of said spectrometric data.7. A method as claimed in claim 1, wherein said method is performed onan in vitro or ex vivo cell population in need of authentication, andthe method comprises analysing said spectrometric data in order to: (i)confirm the authenticity of the in vitro or ex vivo cell population;(ii) detect a mutation in said in vitro or ex vivo cell population; or(iii) to detect an undesired variation in said in vitro or ex vivo cellpopulation.
 8. A method as claimed in claim 1, wherein said methodcomprises analysing said spectrometric data in order: (i) to determinewhether or not said in vitro or ex vivo cell population suffers from aninfection; (ii) to determine whether or not said in vitro or ex vivocell population is infection free; (iii) to determine whether or notsaid in vitro or ex vivo cell population has been cured of an infection;(iv) to determine the progression or stage of an infection of an invitro or ex vivo cell population; and/or (v) to determine theprogression or stage of a treatment for an infection of an in vitro orex vivo cell population.
 9. A method as claimed in claim 1 wherein saidinfection is or comprises a bacterial infection, a mycoplasma infectionor a viral infection.
 10. A method as claimed in claim 1, wherein saidmethod comprises analysing said spectrometric data in order to analysethe effect of a genotype and/or phenotype manipulation on a cellularprocess, a disease, drug production by an in vitro or ex vivo cellpopulation, and/or the response of an in vitro or ex vivo cellpopulation to a substance and/or environmental condition.
 11. A methodas claimed in claim 1, wherein said method comprises a screening method,or wherein said method is used for drug discovery and/or drug analysis.12. A method as claimed in claim 1, wherein said target is a firsttarget sample comprising a first population of cells and saidspectrometric data is first spectrometric data and wherein the methodfurther comprises: generating aerosol, smoke or vapour from a seconddifferent target sample comprising a second population of cells; massanalysing and/or ion mobility analysing aerosol, smoke or vapourgenerated from the second target sample, or ions derived therefrom, soas to obtain second spectrometric data; and comparing said first andsecond spectrometric data to determine differences between said firstand the second target samples.
 13. A method as claimed in claim 12,wherein said first cell population comprises a first geneticmodification and said second cell population does not comprise saidfirst genetic modification, and said method comprises analysing saidfirst and second spectrometric data to analyse the effect of said firstgenetic modification.
 14. A method as claimed in claim 12, wherein saidfirst cell population comprises a first genetic modification and saidsecond cell population comprises a second genetic modification, and saidmethod comprises analysing said first and second spectrometric data toanalyse the effect of said second genetic modification.
 15. A method asclaimed in claim 1, wherein said method comprises manipulating thegenotype and/or phenotype of the in vitro or ex vivo target cellpopulation prior to step (a) of claim
 1. 16. A method according to claim1, wherein said genotype and/or phenotype manipulation or mutagenesis israndom or targeted mutagenesis.
 17. Apparatus comprising: a first devicefor generating smoke, aerosol or vapour from a target in vitro or exvivo cell population and/or culture medium derived therefrom; a seconddevice configured to add a matrix to said aerosol, smoke or vapour,wherein said matrix comprises isopropanol; a mass spectrometer and/orion mobility spectrometer for analysing said smoke, aerosol or vapour,or ions derived therefrom, in order to obtain spectrometric data; and aprocessor adapted to analyse said spectrometric data in order toidentify and/or characterise said target in vitro or ex vivo cellpopulation or one or more cells and/or compounds present in said targetin vitro or ex vivo cell population and/or culture medium derivedtherefrom; wherein said analysis comprises analysing said spectrometricdata in order to: analyse whether there is an infection of said in vitroor ex vivo cell population; or analyse the genotype and/or phenotype ofsaid in vitro or ex vivo cell population or one or more cell typespresent therein; or analyse the effect of manipulating the genotypeand/or phenotype of said in vitro or ex vivo cell population or one ormore cell types present therein.